ENERGY OPTIMIZATION FOR HEATING, VENTILATION, AND AIR-CONDITIONING (HVAC)

Description

TECHNICAL FIELD

The present subject matter relates, in general, to energy management, and in particular, to energy optimization for heating, ventilation, and air-conditioning (HVAC) in a facility.

BACKGROUND

Heating, Ventilation, and Air Conditioning (HVAC) systems are integral components utilized in facilities, such as large-scale commercial buildings, residential complexes, and workspaces. HVAC systems are responsible for maintaining indoor environmental quality and are designed to provide ambient thermal comfort for occupants while ensuring proper ventilation and maintaining acceptable indoor air quality. HVAC systems may vary in complexity and size, ranging from simple residential setups to systems designed for large commercial or public buildings.

SUMMARY

Aspects of the present subject matter provide techniques for optimizing energy utilized by HVAC systems in a facility.

According to an example of the present subject matter, a method for for optimizing energy utilized by HVAC systems in a facility is provided. The method includes obtaining, by an energy management system, one or more parameters corresponding to a zone from amongst a plurality of zones within the facility. The one or more parameters include occupancy data, facility data, and an occupancy schedule, where the occupancy data indicates a number of occupants present in the zone at a first instance of time. Further, the method includes determining, by the energy management system, an optimum temperature set point of the zone in correspondence to the one or more parameters, and transitioning a current temperature set point of the zone to the optimum temperature set point of the zone, where the transitioning to the optimum temperature set point of the zone is to optimize energy utilization of the zone.

According to another example of the present subject matter, a system for energy management in a facility is provided. The system includes an occupancy assessment module and an energy optimization module. The occupancy assessment module is to obtain one or more parameters corresponding to a zone from amongst a plurality of zones within the facility. The one or more parameters include occupancy data, facility data, and an occupancy schedule, where the occupancy data indicates a number of occupants present in the zone at a first instance of time. Further, the occupancy assessment module is to detect a state of occupancy from amongst a plurality of states of occupancy, where the state of occupancy is detected in correspondence to an occupancy percentage computed from the occupancy data. Further, an optimum temperature set point is determined in correspondence to the detected state of occupancy. The energy optimizing module configured to transition a current temperature set point of the zone to the optimum temperature set point, wherein the transitioning to the optimum temperature set point of the zone is to optimize energy utilization of the zone.

According to another example of the present subject matter, a non-transitory computer readable medium containing program instruction is provided, that, when executed, causes the processor to obtain one or more parameters corresponding to a zone from amongst a plurality of zones within the facility, where the one or more parameters include occupancy data, facility data, and an occupancy schedule, and where the occupancy data indicates a number of occupants present in the zone at a first instance of time, determine an optimum temperature set point of the zone and an optimum VAV flow rate of the zone in correspondence to the one or more parameters, and transition a current temperature set point of the zone to the optimum temperature set point of the zone and a current VAV flow rate of the zone to the optimum VAV flow rate of the zone, where the transitioning to the optimum temperature set point of the zone and transitioning optimum VAV flow rate of the zone is to optimize energy utilization of the zone.

BRIEF DESCRIPTION OF DRAWINGS

The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to reference like features and components.

FIG. 1 illustrates a supply chain network environment, in accordance with an example implementation of the present subject matter.

FIG. 2 illustrates an example supply chain network, in accordance with an example implementation of the present subject matter.

FIG. 3 illustrates an energy management system, in accordance with an example implantation of the present subject matter.

FIG. 4 illustrates transitioning of a temperature set value, in accordance with an example implementation of the present subject matter.

FIG. 5 illustrates energy optimization of a zone, in accordance with an example implementation of the present subject matter.

FIG. 6 illustrates an example of energy optimization, in accordance with an example implementation of the present subject matter.

FIG. 7 illustrates another example for energy optimization, in accordance with an example implementation of the present subject matter.

FIG. 8 illustrates a method for energy optimization, in accordance with an example implementation of the present subject matter.

FIG. 9 illustrates a method for transitioning a temperature set point for a zone, in accordance with an example implementation of the present subject matter.

FIG. 10 illustrates another example method for energy optimization, in accordance with an example implementation of the present subject matter.

FIG. 11 illustrates a non-transitory computer-readable medium for energy optimization in a facility, in accordance with an example implementation of the present subject matter.

DETAILED DESCRIPTION

The present subject matter relates to techniques for optimizing energy utilized by HVAC systems in a facility. Typically, HVAC systems are utilized in facilities for energy management with an aim to reduce energy consumption. HVAC systems employed in facilities, such as commercial office buildings, multistorey residential complexes, educational institutions, data centers, and the like, include multiple components interconnected with one another to provide conditioned and ambient air to the facility. Generally, HVAC systems include air handlers, or an Air Handling Unit (AHU), to condition the atmospheric air before supplying the same indoors. AHUs typically include components such as filters, humidifiers, heating coils, cooling coils, dampers, and the like to condition the outside air which is then supplied to different areas of the facility in a controlled manner. HVAC systems usually condition the air external to the facility by controlling parameters such as temperature, pressure, humidity, and the like. Additionally, these systems are equipped with a supply duct network for distributing conditioned air throughout the facility and an exhaust duct network for expelling indoor air, thereby ensuring that the facilities are well-ventilated.

Performance of HVAC systems is influenced by a variety of dynamic factors, including environmental conditions, facility characteristics, technological integration, and the like. Traditional HVAC systems often rely on thermostat-based controls, which use static temperature set points to condition the air being supplied to the facility. For example, in a cafeteria of a facility, the temperature set point may be pre-set to a value of 22 degrees C. The HVAC system may try to adjust the temperature to this temperature set point based on a difference in the actual temperature levels observed in the cafeteria and the pre-set temperature set point. However, such techniques generally do not account for the various environmental conditions that may influence the facility and also do not consider the fluctuating nature of occupancy, which can lead to either inadequate conditioning or over conditioning of differently occupied spaces.

Additionally, the inability to respond to unexpected changes in occupancy levels may lead to excessive energy consumption by the HVAC systems. Occupancy within the facility, being highly dynamic in nature, may impact internal temperatures of the facility and may therefore either cause the HVAC systems to overwork or underwork to stabilize the internal temperatures to pre-set temperature set points. Constant effort of stabilization of the internal temperatures of the facility to pre-set temperature set points causes continues utilization of energy by the HVAC systems.

Further, such static settings lead to user having to manually adjust thermostats, leading to suboptimal energy consumptions by the HVAC systems. Moreover, fixed temperature set points do not accommodate individual preferences, thereby negatively impacting occupant's comfort. Thus, the inability to dynamically adjust temperature set points while considering different operation conditions leads inefficient utilization of the HVAC systems, causing faster wear and tear, and also increases operational costs of the HVAC systems.

Conventional HVAC systems typically tend to optimize temperatures in the facility as a whole, often neglecting the influence of external factors on specific areas or zones within the facility. This oversight can cause the HVAC system to exert more effort to maintain temperature set points, leading to higher energy consumptions and increased strain on HVAC's system components. Operating under suboptimal conditions without considering such factors can accelerate the ageing process and lead to premature aging and failure of HVAC components.

According to examples of the present subject matter, techniques to optimize energy utilized by HVAC systems in a facility are provided. For the ease of understanding, the following description has been explained with reference to the facility relating to a supply chain, where the facility may be an industrial building, a warehouse, a manufacturing plant, a commercial building, a residential building, and the like. Each of these facilities may be divided into multiple zones, where each zone may be designed differently, and exposed to different environmental conditions. For example, a commercial building may have three zones, where the first zone may be the lobby area, the second zone may be where all the workstations are located and the third zone may be the cafeteria. Accordingly, the structural designs, the heating and cooling requirements, thermal loads of the zone, occupancy levels, occupancy schedules, facility data, and the like may be different for each of the three zones.

In operation, techniques of the present subject matter obtain one or more parameters corresponding to a zone from amongst a plurality of zones within the facility. The one or more parameters may include occupancy data, facility data, an occupancy schedule, and the like. Occupancy data, for instance, quantifies the number of individuals present in a zone at a given time. For example, occupancy data obtained for the first zone at 5 pm may indicate the number of occupants present in the first zone at that time. In one example, the occupancy data for the zone may be obtained at regular time intervals, for example, every 10 minutes or every 15 minutes.

Based on analysis of the obtained parameters, an optimum temperature set point for the zone may be determined. In correspondence to the determining the optimum temperature set point, a current temperature set point of the zone may be transitioned to the optimum temperature set point of the zone, where the transitioning to the optimum temperature set point of the zone is to optimize the energy utilization of the zone.

In one aspect, the optimum temperature set point may be determined by detecting a state of occupancy for each zone. The state of occupancy may be detected based on occupancy data. In an example, an occupancy percentage may computed for each zone of the facility based on the occupancy data. An occupancy percentage for a zone represents the ratio of the number of occupants present at a given instance, derived from the occupancy data, to the total number of occupants the zone can accommodate.

In one example, the state of occupancy of a zone may be detected based on occupancy percentage. For example, a predefined range of occupancy percentages computed for the zone at a given point of time may correspond to a state of occupancy. In an example, an occupancy percentage within the range of 1-20 percent may correspond to a ‘state A’ occupancy level. Similarly, an occupancy percentage within the range of 21-40 percent may correspond to ‘state B’, a range of 41-60 percent to ‘state C’, a range of 61-80 percent to ‘state D’, and a range of 81-100 percent to ‘state E’. When the occupancy reaches 100 percent, the state of occupancy may be classified as ‘state F’.

On detecting the state of occupancy, a transition value to transition the current temperature set point to the optimum temperature set point may be identified. In one example, the transition value may be determined based on a predetermined threshold value corresponding to the states of occupancy. Once the optimum temperature set point is identified, the temperature set point of the zone may be incrementally adjusted from the current temperature set point to the optimum temperature set point. For example, a zone's temperature set point may be adjusted in response to varying occupancy levels to achieve energy savings while maintaining occupant comfort. Consider a scenario where the zone temperature set point for a meeting room in a commercial building is set to 22 degrees Celsius at 9 AM, accommodating the design occupancy of 20 individuals. However, during lunch hours from 12:30 to 1:00 PM, the occupancy count may reduce to just 2 individuals. In response to the reduced occupancy, the current temperature set point may be increased from 22 degrees Celsius to an optimum temperature set point of 24 degrees Celsius for the meeting room. Therefore, as occupancy shifts between states, techniques of the present subject matter adjust the temperature set point dynamically to optimize energy utilization of the zone.

In another example, the optimum temperature set point for a zone may be predicted based on an occupancy schedule of the said zone. For instance, if the occupancy schedule indicates that a conference room is typically fully occupied during morning hours, the optimum temperature set point may be adjusted in anticipation of the pattern to ensure comfort when the room is in use.

In another example, the optimum temperature set point of the zone may be determined based on the one or more parameters obtained and historical transition data associated with the said zone. The historical transition data may encompass information pertaining to changes in the zone's temperature set points, Variable Air Volume (VAV) flow rates, Air Handling Unit (AHU) supply air flow rates, and the one or more parameters associated with the zone. For example, if historical transition data reveals that a training room's temperature set point is frequently lowered by 2 degrees Celsius after 3 PM due to reduced occupancy and lower solar heat gain, the temperature set point of the said zone may be proactively adjusted to optimize for energy efficiency while maintaining occupant comfort.

Additionally, in one example, transitioning the current temperature set point of the zone to the optimum temperature set point of the zone may further include monitoring a predicted mean vote (PMV) of the zone to ensure thermal comfort. In an example, the predicted mean vote of the zone is computed in correspondence to an air temperature value of the zone, a mean radiant temperature of the zone, air velocity of the zone, relative humidity of the zone, activity level in the zone, and clothing insulation.

In one example, a Variable Air Volume (VAV) flow rate of the zone or an Air Handling Unit (AHU) supply air rate may also be controlled in correspondence to the optimum temperature set point determined for the zone.

In one example, the occupancy data may be monitored for a first time period, such as every 15 minutes, to facilitate the transitioning of the current temperature set point of the zone to the optimum temperature set point of the zone.

Additionally, the one or more parameters may include a zone recovery time, where the zone recovery time corresponds to the time taken for transitioning the current temperature set point to the optimum temperature set point. In one example, each zone within the facility may be assigned a priority based on the zone recovery time. For instance, a zone that is consistently exposed to sunlight may require a longer period to transition its temperature set point compared to a zone with little to no sunlight exposure. Consequently, zones with extended recovery times, like those with substantial sunlight exposure, may be given a lower priority in the energy conservation strategy to enhance efficiency.

Further, in one example, the one or more parameters and the current temperature set point of the zone may be analyzed after every predefined time intervals to determine a new optimum temperature set point of the zone, and transition the current temperature set point of the zone to the new optimum temperature set point of the zone.

In one example, a Variable Air Volume (VAV) flow rate of the zone may also be regulated to enhance optimizing the energy utilization of the zone. In one example, an optimum VAV flow rate for the zone may be identified in accordance with the one or more parameters. Accordingly, the current VAV flow rate for the zone may be transitioned to the optimum VAV flow rate for the zone, where transitioning to the optimum VAV flow rate for the zone is to optimize the energy utilization of the zone.

In one example, the one or more parameters and the current VAV flow rate may be analyzed at pre-defined time intervals to identify a new optimum VAV flow rate for the zone and the current VAV flow rate of the zone may be transitioned to the new optimum VAV flow rate for the zone.

Furthermore, in one example, at least a first optimum VAV flow rate for a first zone amongst the plurality of zones within the facility and a second optimum VAV flow rate for a second zone amongst the plurality of zones within the facility is determined, where the first zone and the second zone may be coupled to at least one VAV box. Therefore, techniques of the present subject matter facilitate optimizing the VAV flow rate across multiple zones. For example, if both zones are occupied, the VAV box may increase its total airflow to satisfy the combined demand while individually regulating dampers to maintain the specific temperature set points for each zone. Conversely, if one zone is unoccupied while the other is in use, the VAV box can decrease its total airflow to conserve energy, directing the bulk of the airflow to the zone that is occupied. Thus, by regulating the VAV flow rate in real-time, the system can facilitate energy conservation for each zone within the facility.

The present subject matter thus provides a dynamic method for optimizing energy consumption in HVAC systems by adjusting temperature set points and VAV flow rates based on real-time occupancy data. Further, by dynamically adjusting HVAC operations based on real-time occupancy data, techniques of the present subject matter reduce energy consumption in underutilized zones, leading to more efficient use of energy resources, thereby resulting in substantial energy savings, particularly in large commercial buildings where occupancy levels can vary greatly throughout the day. Reduced energy consumption directly translates into lower utility bills, thereby facilitating substantial cost savings over time. Also, by optimizing HVAC systems to use less energy, techniques of the present subject matter help reduce the overall carbon footprint of the facility, contributing to environmental sustainability efforts. Additionally, by integrating the Predicted Mean Vote (PMV) techniques of the present subject matter ensure that adjustments to temperature set points and air flow rates do not compromise occupant comfort. Furthermore, techniques of the present subject matter facilitate optimizing the VAV flow rate across multiple zones to further enhance optimization of energy utilization of the facility.

The above and other features, aspects, and advantages of the subject matter will be explained with regard to the following description and accompanying figures. It should be noted that the description and figures merely illustrate the principles of the present subject matter along with examples described herein and should not be construed as a limitation to the present subject matter. It is thus understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and examples thereof, are intended to encompass equivalents thereof. Further, for the sake of simplicity, and without limitation, the same numbers are used throughout the drawings to reference like features and components.

FIG. 1 illustrates a supply chain network environment 100, in accordance with an example implementation of the present subject matter. In one example, the supply chain network environment 100 may include a supply chain network 102 including multiple facilities, 104-1, 104-2, 104-3, . . . 104-n, collectively and alternatively referred to as multiple facilities 104 or facility 104. For example, but not limited to, the facility 104 may be a commercial office building, a multistorey residential complex, educational institutions, data centers, airports, retail spaces, hospital and healthcare facilities, a warehouse in a packaging industry, an assembling unit of an automobile manufacturing company, a consumer-goods manufacturing unit, an e-commerce storage unit, a cold storage of a food manufacturing company, a pharmaceutical manufacturing unit, and the like. In one example, the multiple facilities 104 may be distributed across different locations in the supply chain network 102.

Each facility of the multiple facilities 104 may include a facility management system (not shown in the figure). In one example, the facility management system may be employed in each facility 104 for energy management. In one example, the facility management system may be part of a source device (not shown in the figure), where the source device may be an Internet of things (IoT) device, a computing device, a personal computer, a laptop, a tablet, a mobile phone, and the like. In another example, the facility management system may be hosted on a server (not shown in the figure) that may communicate with the source device.

In one example, the facility management system of each of the multiple facilities 104 may be communicatively coupled to an energy management system 106. The facility management systems and the energy management system 106 may communicate over a network 108. The network 108 may be a wireless network or a combination of a wired and wireless network. The network 108 can also include a collection of individual networks, interconnected with each other and functioning as a single large network, such as the Internet. Examples of such individual networks include, but are not limited to, Global System for Mobile Communication (GSM) network, Universal Mobile Telecommunications System (UMTS) network, Personal Communications Service (PCS) network, Time Division Multiple Access (TDMA) network, Code Division Multiple Access (CDMA) network, Next Generation Network (NGN), Public Switched Telephone Network (PSTN), Long Term Evolution (LTE), and Integrated Services Digital Network (ISDN). Depending on the terminology, the communication network includes various network entities, such as gateways and routers; however, such details have been omitted to maintain the brevity of the description.

Further, the energy management system 106 may be implemented in any computing system, such as a storage array, a server, a desktop or a laptop, a computing device, a distributed computing system, or the like. Although not depicted, the energy management system 106 may include other components, such as interfaces to communicate over the network or with external storage or computing devices, display, input/output interfaces, operating systems, applications, data, and other software or hardware components (not depicted for the sake of brevity).

In one example, the energy management system 106 is to optimize the energy utilized across each facility 104 of the supply chain network 102. In one example, each of these facilities 104 within the supply chain network 102 may be equipped with an Air Handling Unit (AHU) 120, as represented in 118. The AHU 120 may be responsible for supplying conditioned air to the respective facility. The AHU 120 may condition the air being supplied to each facility by modifying the temperature, pressure, humidity, and the like, of the air extracted from the environment to meet the requirements of the facility. The conditioned air being supplied to each facility 104 is then constantly regulated and conditioned to maintain an acceptable indoor air quality and also ensure optimal thermal comfort for occupants of the facility 104.

In one example, each facility 104 of the supply chain network may be further divided into multiple zones. For example, but not limited to, Facility 104-1 may be divided into three zones, represented as Zone 1, Zone 2, and Zone 3, where each zone corresponds to a specific area of the facility 104-1. In one example, the AHU 120 may condition the air being supplied to each of these zones. In another example, the AHU may be coupled to one or more Variable Air Volume (VAV) boxes 122, installed in each zone of the facility, through which conditioned air may be supplied to each zone of the facility. The VAV boxes 122 of the facility may be coupled to the AHU 120 through a duct network. The duct networks typically include two types of ducts, a supply duct 124 through which conditioned air is supplied to a zone of the facility and an exhaust duct 126 through which air from the zone is expelled.

In one example, the energy management system 106 may obtain data 114-1, 114-2, 114-3, . . . , 114-n, collectively referred to as data 114, from multiple facilities 104-1, 104-2, 104-3, . . . 104-n, respectively. In one example, the data 114 generated by the multiple facilities 104, amongst other information, may include information associated with airflow rates, supply air temperature, return air temperature, zone temperatures, occupancy count, damper position data, static pressure, humidity levels, energy consumption, ducts temperature and pressure, carbon-di-oxide concentration levels, timings of operations, faults and alarm status, facility design details, zone segregation details of the facility, historical data, environmental factors influencing the facility, geographical location of the facility, thermal loads, zone orientations, and the like. For example, in a facility, such as a commercial building, the data 114 could indicate different zones of the building, design data for each of the zones, the geographical location of each zone within the facility, an occupancy schedule for the zone, occupancy data associated with the zone, and the like.

On obtaining the data 114 from each of the facilities 104 within the supply chain network 102, the energy management system 106 may analyze the data 114. The energy management system 106 may analyze the data 114 to optimize the energy utilization in each zone within the facility. For example, the energy management system 106 may optimize a temperature set point and a VAV flow rate of the zone of the facility to ensure optimal energy consumption in the said zone. By optimizing the energy utilized at the zone level, techniques of the present subject matter ensure efficient allocation of energy resources within the facility, which results in substantial energy savings at the facility level as well as the overall supply chain network.

FIG. 2 illustrates an example supply chain network 200, in accordance with an example implementation of the present subject matter. In one example, the supply chain network 200 depicts Facility 104-1 and Facility 104-n communicatively coupled to the energy management system 106. For the sake of simplicity, the following description has been predominantly discussed with reference to Facility 104-1 and Facility 104-n of the supply chain network 200, communicatively coupled to the energy management system 106. However, similar principles may be applicable to all facilities of a supply chain network 200 coupled to the energy management system 106.

In one example, Facility 104-1 of the supply chain network 200 may be an office building located in a first geographical location and Facility 104-n may be an industrial site located in a second geographical location of the supply chain network 200. Each of the facilities, Facility 104-1 and Facility 104-n, make include multiple zones, with different heating, ventilation, and air conditioning requirements. Each zone of the facility may correspond to a specific area within the facility, that may be positioned, for example, on the same floor of the facility, or may span across multiple floors of the facility, and the like. For instance, within the Facility 104-1, Zone 1 may be a lobby area located on the ground floor. On the first floor, Zone 2 may be a conference room and Zone 3 may be an open office space with multiple workstations, where Zone 2 and Zone 3 are adjacent to one another. Similarly, on the second floor, Zone 4 may be a cafeteria and Zone may be another open office space with multiple workstations, where both these zones are also adjacent to one another. Similarly, Facility 104-n may be composed of three zones located adjacent to one another, where Zone 1 may be a cleanroom area, Zone 2 may be a shop floor, and Zone 3 may be an open office space with multiple workstations.

Each of these zones within Facility 104-1 and Facility 104-n may differ from one another with respect to ambient light levels due to natural lighting, heat generated by equipments in the zone, heat dissipated from equipments installed on the shop floor, heat dissipated from the equipment when in operation, thermal loading, humidity levels of the zone, the HVAC design for the corresponding zone, the positioning of the vents connected to the ducts supplying and collecting air from the said zone, an occupancy schedule of the zone, real-time occupancy data of the zone, historical occupancy data, historical occupancy schedule records, external environmental factors influencing the facility, and the like.

In one example, each facility may include a facility management system 202-1, 202-n, respectively. In one example, the facility management system 202-1 of Facility 104-1 and the facility management system 202-n of Facility 104-n may be communicatively coupled to the energy management system 106. Data A and data B from each of these zones may be collected by the facility management systems 202-1 and 202-n, respectively.

For the sake of simplicity, the following description has been discussed with reference to the facility management system 202-1 of Facility 104-1, of the supply chain network 102. However, it may be understood that similar principles may be applicable to all other facilities 104 within the supply chain network 102. In one example, the facility management system 202-1 includes a processor 206 and a memory 208. The processor(s) 206 may be provided through the use of dedicated hardware as well as hardware capable of executing instructions. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” would not be construed to refer exclusively to hardware capable of executing instructions, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing instructions, random access memory (RAM), non-volatile storage. Other hardware, standard and/or custom, may also be included. The memory 208 may include any computer-readable medium including, for example, volatile memory (e.g., RAM), and/or non-volatile memory (e.g., EPROM, flash memory, etc.).

The facility management system 202-1 may further include modules 210, such as energy assessment modules, asset monitoring modules, data ingestion modules, and the like (not shown). In one example, the modules 210 may be implemented as a combination of hardware and firmware. In examples described herein, such combinations of hardware and firmware may be implemented in several different ways. For example, the firmware for the module 210 may be processor 206 executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the module 210 may include a processing resource (for example, implemented as either a single processor or a combination of multiple processors), to execute such instructions.

In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the functionalities of the modules 210. In such examples, the facility management system 202-1 may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions. In other examples of the present subject matter, the machine-readable storage medium may be located at a different location but accessible to the facility management system 202-1 and the processor(s) 206.

The facility management system 202-1 may further include a database 212, that serves, amongst other things, as a repository for storing data A that may be fetched, processed, received, or generated by the modules. For example, but not limited to, data A associated with the zones of Facility 104-1 may include information regarding one or parameters of the zone such as location of the zone, dimensions of the zone, orientation and structural data of Facility 104-1, environmental factors impacting the facility and the zone, thermal loading of the zone, occupancy levels of the zone, occupancy schedules for the zone, operational timing schedules for the zone, and the like. In one example,

In one example, the facility management system 202a of Facility 104-1 may integrate and store all the data A collected from multiple zones within the facility, as data 314 of the facility management system 202a. Similarly, data B from multiple zones within Facility 104-n of the supply chain network 102 may be collected and stored in the facility management system 202-n. In one example, data A from Facility 104-1, data B from Facility 104-n of the supply chain network 102 may be communicated to energy management system 106.

Based on such data obtained from the facility management systems 202-1 and 202-n, the energy management system 106 may analyze the data to optimize the energy utilized across the multiple zones within Facility 104-1 and Facility 104-n, respectively. In one example, the energy management system 106 may analyze the data to transition one or more HVAC parameters such as a temperature set point, a Variable Air Volume (VAV) flow rate, an Air Handling Unit (AHU) supply air rate, and the like to their optimum values to enhance the optimization of energy across multiple zones within a facility.

For instance, considering the examples discussed above, Facility 104-1 may experience a high occupancy level in Zone 5, which is the open office space with multiple workstations, during regular business hours, leading to increased thermal loading and a demand for more cooling. Zone 5 may also have an increased demand for cooling since Zone 5 is located adjacent to the cafeteria, where excess heat generated from Zone 4 may influence the temperature of Zone 5. Simultaneously, Zone 1, the lobby area, may have lower occupancy and since Zone 1 and also, since Zone 1 is located on the ground floor, Zone 1 may require less cooling. The energy management system 106, by analyzing data A from the facility management system 202-1, could optimize the HVAC parameters for each zone, by increasing airflow to Zone 5 while reducing it in Zone 1 to conserve energy while maintaining comfort.

Similarly, Zone 2 of Facility 104-n, which is a shop floor, may operate heavy machinery that generates substantial heat, necessitating enhanced ventilation and cooling. On the other hand, Zone 1, the cleanroom area, might require strict temperature and humidity control due to sensitive processes taking place in the said zone. The facility management system 202-n, in communication with the energy management system 106, could adjust the HVAC design parameters to ensure Zone 2 receives adequate cooling to offset the heat generated by equipment, while maintaining the precise environmental conditions in Zone 1 for operational integrity.

Therefore, the energy management system dynamically regulates the temperature set points, VAV flow rates, supply air rates, and the like, to their optimum values, taking into account the facility data, occupancy data, occupancy schedules, and the like, associated with each zone of the facility for optimal energy utilization in each of these zones. The energy management system 106 has been discussed with reference to FIG. 3.

FIG. 3 illustrates an energy management system 106, in accordance with an example implementation of the present subject matter. In one example, the energy management system 106, alternatively referred to as system 106, may optimize energy utilization for a facility. In one example, the energy management system 106 may include a processor 302 and a memory 304 coupled to the processor 302. The functions of functional block labelled as “processor(s)”, may be provided through the use of dedicated hardware as well as hardware capable of executing instructions. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term “processor” would not be construed to refer exclusively to hardware capable of executing instructions, and may implicitly include, without limitation, digital signal processor (DSP) hardware, network processor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), read only memory (ROM) for storing instructions, random access memory (RAM), non-volatile storage. Other hardware, standard and/or custom, may also be included. Further, an interface(s) 306 may allow the connection or coupling of the system 106 with one or more other devices (say devices or systems within the supply chain network), through a wired (e.g., Local Area Network, i.e., LAN) connection or through a wireless connection (e.g., Bluetooth®, Wi-Fi). The interface(s) 306 may also enable intercommunication between different logical as well as hardware components of the system 106.

The memory 304 may include any computer-readable medium including, for example, volatile memory (e.g., RAM), and/or non-volatile memory (e.g., EPROM, flash memory, etc.).

The energy management system 106 may further include modules 308, such as an occupancy assessment module 310 and an energy optimizer 312. In one example, module(s) 308 may be implemented as a combination of hardware and firmware. In examples described herein, such combinations of hardware and firmware may be implemented in several different ways. For example, the firmware for the module may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the module may include a processing resource (for example, implemented as either a single processor or a combination of multiple processors), to execute such instructions.

In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the functionalities of the module(s) 308. In such examples, the energy management system 106 may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions. In other examples of the present subject matter, the machine-readable storage medium may be located at a different location but accessible to the energy management system 106 and the processor 302.

The energy management system 106 may further include data 314, that serves, amongst other things, as a repository for storing data that may be fetched, processed, received, or generated by the modules 308. The data 314 may include communication data, location data of each facility, occupancy data associated with each zone of the facility, design data corresponding to each facility, temperature levels in each zone, thermal comfort levels, threshold data, and the like. In an example, the data 314 may be stored in the memory 304.

In one example, to optimize the energy utilized in each zone within the facility, the energy management system 106 may monitor and dynamically control one or more HVAC parameters of a zone, such as a temperature set point of the zone, a supply flow rate for the zone, a VAV flow rate of the zone, and the like. These parameters may be increased or decreased based on the heating or cooling requirements of the said zone.

In one example, to optimize the energy utilized in each zone, the occupancy assessment module 310 of the energy management system 106 may obtain one or more parameters corresponding to each zone from amongst a plurality of zones within the facility. In one example, the one or more parameters may include facility data, occupancy data, occupancy schedules, and the like. In one example, the facility data may include information regarding the type of facility, location of the facility, site complexity, external environmental factors influencing the facility, HVAC design adopted for the facility, HVAC design adopted for the zone of the facility, location of the zone, external environmental factors influencing the zone, regions of a zone with reduced exposure to natural light, and the like.

Further, occupancy data of each zone may indicate a number of occupants present in the said zone at a first instance of time. In one example, the occupancy data may be obtained in real-time. Further, the occupancy schedule obtained for a zone may indicate a possible number of occupants who could be present in the said zone in a pre-defined time slot. For example, an occupancy schedule for a zone such as a conference room may indicate that 20 occupants are expected to be present between the 2:00 pm and 4:00 pm on Tuesday, and the like. In one example, the occupancy schedules for a zone can be updated and accessed in real-time, allowing for dynamic adjustments to energy management strategies.

On obtaining the one or more parameters for a zone of the facility, the energy management system may optimize a HVAC parameter such as amongst other parameters, a temperature set point of the zone, a Variable Air Volume (VAV) flow rate, an Air Handling Unit (AHU) supply air rate, and the like. Although the following description has been described primarily with respect to the temperature set point and VAV flow rates, as would be understood, similar principles of the present subject matter outlined herein would be applicable to other HVAC parameters. In one example, based on the one or more parameters obtained, the energy optimizer 312 of the system 106 may determine an optimum temperature set point of the zone. The temperature set point of the zone is the temperature at which a thermostat of the zone is set for optimal thermal comfort of an occupant in the zone.

In one example to determine the optimum temperature set point of the said zone, an occupancy percentage for the zone may be computed. An occupancy percentage for a zone represents the ratio of the number of occupants present at a given instance, derived from the occupancy data, to the total number of occupants the zone can accommodate. For example, if a zone has a full design capacity to accommodate 100 occupants and at a given point of time there are 20 occupants present, the occupancy percentage for that zone would be calculated to be 20%.

In one example, a state of occupancy from amongst multiple states of occupancy may be detected for a zone. In one example, the state of occupancy may correspond to a predefined range of occupancy percentages computed for the zone at a given point of time. For example, but not limited to, an occupancy percentage within the range of 1-20 percent may indicate a ‘state A’ occupancy level. Similarly, an occupancy percentage within the range of 21-40 percent may correspond to ‘state B’, a range of 41-60 percent to ‘state C’, a range of 61-80 percent to ‘state D’, and a range of 81-100 percent to ‘state E’. When the occupancy reaches 100 percent, the state of occupancy may be classified as ‘state F’.

On detecting the state of occupancy, a transition value to transition a current temperature set point of the zone to the optimum temperature set point determined may be identified. In one example, transitioning the current temperature set point of the zone to the optimum temperature set point may occur by increasing or decreasing the current temperature set point of the zone to the optimum temperature set point of the zone based on a heating requirement or a cooling requirement in the zone. In one example, the transition value may be determined based on a predetermined threshold value corresponding to the states of occupancy.

In one example, each state of occupancy may be associated with a pre-determined threshold value. For example, state ‘A’ may be associated with a pre-determined threshold value of ‘v’ degrees. Similarly, state ‘B’ may be associated with a pre-determined threshold value of ‘w’ degrees, state ‘C’ may be associated with a pre-determined threshold value of ‘x’ degrees, state ‘D’ may be associated with a pre-determined threshold value of ‘y’ degrees, and state ‘E’ may be associated with a pre-determined threshold value of ‘z’ degrees. As would be understood, since state ‘F’ corresponds to full occupancy, the optimum temperature set point at state ‘F’ would be predetermined in accordance with one or more parameters associated with the zone. In one example, all the pre-determined threshold values associated with each state of occupancy may be equal to one another. For example, the values of v, w, x, y, and z may be set to 0.5 degrees each. In another example, the values of v, w, x, y, and z may vary from one another.

In one example, based on these pre-determined threshold values, a transition value to transition the current temperature set point of the zone to the optimum temperature set point of the zone may be determined. In one example, the transition value to transition the current temperature set point to the optimum temperature set point may be computed by the following equation as represented below in equation (1):

Transition value=Sum of the predetermined threshold values associated with states of occupancy that lie between a preceding state of occupancy and the detected state of occupancy (1)

Where the preceding state of occupancy corresponds to the state of occupancy at the current temperature set point. Accordingly, the optimum temperature set point may be determined by increasing or decreasing the current temperature set point by the transition value based on the heating or cooling requirement of the zone. FIG. 4 illustrates transitioning 400 of a temperature set point, in accordance with an example implementation of the present subject matter. The following example is only to illustrate principles of the present subject matter and is not to be construed as a limitation.

For instance, in an office building, the office space that includes all the workstations may be at full occupancy at 9 AM. The full occupancy may be designated as state ‘F’, and the optimum temperature set point for this state could be, for example, 22 degrees Celsius. Conversely, between 12:30 PM and 1:00 PM, the occupancy level might decrease to 10 percent, corresponding to state ‘A’. The optimum temperature set point for state ‘A’ would then be established based on this reduced occupancy level.

On considering that the pre-determined threshold values associated with each state of occupancy is set to 0.5 degrees. The optimum temperature set point for state ‘A’ may be computed by determining the transition value from equation (1) represented above. Since the state of occupancy at the current temperature set point is state ‘F’ and the detected state of occupancy is state ‘A’, the transition value would be computed as a sum of the predetermined threshold values associated with states of occupancy that lie between a preceding state of occupancy, state ‘F’, and the detected state of occupancy, state ‘A’, which would be 2.5 degrees. Therefore, the current temperature set point of the office space is transitioned from 22 degrees C. to the optimum temperature set point of 24.5 degrees C. Therefore, the optimum temperature set point for state ‘A’ of occupancy would be 24.5 degrees to optimize energy utilization of the zone at 10% occupancy. In one example, the transitioning of the current temperature set point to the optimum temperature set point may be performed incrementally to maintain thermal comfort and energy efficiency. For example, transitioning the temperature set point from state ‘F’ to state ‘A’ may be performed incrementally by a predefined temperature offset of 0.5 degrees from one state to the next.

Therefore, as occupancy shifts between states, techniques of the present subject matter adjust the temperature setpoint dynamically to optimize energy utilization of the zone.

Continuing with the description of FIG. 3, in one example, the system 106 may monitor the occupancy data of the zone for a first time period to transition the current temperature set point of the zone to the optimum temperature set point of the zone. For example, the first time period may be 15 minutes, or 10 minutes, and the like. That is, in one example, the occupancy data may be monitored for the first period of time, and if it is determined that the occupancy data is constant for the first period of time, the transition of the current temperature set point to the optimum temperature set point takes place.

In one example, the system 106 may analyze the one or more parameters and the current temperature set point of the zone at pre-defined time intervals to identify a new optimum temperature set point of the zone, and accordingly transition the current temperature set point of the zone to the new optimum temperature set point of the zone. In one example, the one or more parameters and the current temperature set point for the zone may be determined every 15 minutes. In another example, the one or more parameters and the current temperature set point may be monitored in real-time to ensure dynamic optimization of energy utilization.

Furthermore, in one embodiment, the transition of the current temperature set point of a zone to the optimum temperature set point may involve monitoring the predicted mean vote (PMV) to ensure thermal comfort within the zone. The PMV is a measure that can be used to assess the thermal comfort of the occupants based on various environmental and personal factors. In this embodiment, the PMV for a zone may be calculated based on parameters such as the air temperature of the zone, the mean radiant temperature, air velocity, relative humidity, the activity level within the zone, and the clothing insulation of the occupants. As understood by those skilled in the art, the PMV is typically measured on a scale ranging from −3 to +3, where −3 signifies an environment perceived as too cold and +3 as too hot. Ideally, maintaining a PMV of 0 would indicate an environment with optimum thermal comfort for the occupants.

In one example, but not limited to, on determining the optimum temperature set point for the zone, the PMV value may be monitored in real-time. If the PMV value is within the range of −1 to +1, which is generally considered acceptable for thermal comfort, the current temperature set point of the zone may be transitioned to the optimum temperature set point. This regulation ensures that the thermal comfort of the occupants is maintained while also optimizing the energy utilization of the HVAC system within the zone.

In another example, determining the optimal temperature set point for the zone may be performed by predicting the optimal temperature set point based on the occupancy schedule of the zone. For example, an office building with a conference room that is used for meetings according to a pre-determined schedule may be considered. The occupancy schedule may indicate that the conference room is used every weekday from 10:00 am to 11:00 am and from 3:00 pm to 4:00 pm. Outside of these scheduled meeting times, the conference room typically remains unoccupied. In order to optimize the energy utilization of the conference room based on this schedule, the system 106 may maintain a baseline temperature set point of 24 degrees when the room is unoccupied to conserve energy. However, 30 minutes before the scheduled meeting, the system 106 may begin transitioning the temperature set point to reach optimum temperature set point based on the expected occupancy for the said meeting, thereby ensuring a comfortable environment for the occupants during the meeting. Further, once the meeting concludes, the system may regulate the temperature set point based on the current occupancy level of the room at that instant of time and may gradually return to the temperature set point of 24 degrees. Therefore, the system optimizes the energy utilized while maintaining comfort during times the conference room is in use based on the occupancy schedule as well as the occupancy monitored in real time.

Further, in one example, in addition to optimizing the temperature set point for the zone, the system 106 may also optimize a Variable Air Volume (VAV) flow rate for the zone as described with reference to FIG. 5.

FIG. 5 illustrates energy optimization of a zone, in accordance with an example implementation of the present subject matter. In one example, the optimum temperature set point and an optimum Variable Air Volume (VAV) flow rate for the zone may be determined for a given zone. In one example, these optimum values may be determined from one or more parameters associated with the zone and historical transition data. Historical transition data may encompass information pertaining to past changes in the zone's temperature set points, Variable Air Volume (VAV) flow rates, Air Handling Unit (AHU) supply air flow rates, and the one or more parameters associated with the zone. In one example, by analyzing historical data in conjunction with the current parameters of the zone, the system may determine the optimum values for transitioning the temperature set points and VAV flow rates. The utilization of historical data enables the system to learn from past performance and anticipate future needs, thereby enhancing its ability to optimize the zone's environmental conditions in response to changing occupancy levels and other relevant factors.

In one example, an analyzing module 504 of the energy management system 106 may analyse the one or more parameters associated with the zone and historical transition data to optimize at least one of the temperature set point and the VAV flow rate of the zone. In one example, in order to train the analyzing module 504, data associated with the one or more zones of the facility such as location data, structural design information, data associated with external environmental factors influencing the zone, current occupancy data, historical occupancy data, historical transition information of temperature set points and VAV flow rates for a zone, historical occupancy patterns for a zone, occupancy schedules, seasonal temperature set point adjustments, changes in humidity levels and corresponding HVAC responses, variations in CO2 levels and associated ventilation rate adjustments, fluctuations in energy consumption patterns during different operational modes, past adjustments to fan speeds in response to occupancy changes, records of manual overrides of automated settings by occupants, data on how quickly zones reach desired temperatures after set point changes, historical patterns of thermal load variations throughout the day, past responses to sudden occupancy changes (e.g., unexpected meetings), records of how external weather conditions influenced internal HVAC operations, records of how different zones interact thermally when occupancy patterns change, past patterns of air quality measurements and corresponding HVAC adjustments, past correlations between occupant feedback and HVAC parameter adjustments, records of how different control strategies affected overall building energy consumption, and the like may be provided. This data may be collected from multiple zones across various facilities spanning across the supply chain network, exposed to different scenarios. In one example, a facility management system s may obtain the data from one or more zones that is communicatively coupled to the system 106.

On receiving the data associated with one or more zones of the facility, the analyzing module 504 may analyze the data for different scenarios to optimize the temperature set point of the zone and the VAV flow rate for the zone for utilizing energy efficiently. In one example, but not limited to, the analyzing module 504 may generate various patterns between parameters such as: zone temperature, temperature set point, PMV value, occupancy data, occupancy schedules, and the like. In one example, the analyzing module 504 may employ techniques such as reinforcement learning, stochastic data driven methods, non-linear regression models, and the like to determine the optimum temperature set point and VAV flow rate from the patterns generated. Although the following description has been explained with optimizing HVAC parameters such as temperature set point and VAV flow rates for a zone, similar principles are applicable to other HVAC parameters of the facility.

On deployment of the energy management system 106, the system 106 may various parameters associated with each zone of the facility, for example, facility data, occupancy data of the zone, and an occupancy schedule of the zone. Based on these parameters, the system 106 may analyze the one or more parameters based on patterns generated by the analyzing module 504 which correlate one or more parameters and historical transition data associated with the zone to optimize the temperature set point of the zone as well as a VAV flow rate of the zone. In one example, the energy management system may identify at least one of an optimum temperature set point of the zone and an optimum VAV flow rate of the zone and transition a current temperature set point of the zone to the optimum temperature set point of the zone and a current VAV flow rate of the zone to the optimum VAV flow rate of the zone, where the transitioning to the optimum temperature set point of the zone and transitioning optimum VAV flow rate of the zone is to optimize energy utilization of the zone. Further, in an example where the VAV flow rate of the zone is unavailable, the system 106 may alternatively optimize the supply air rate of the AHU. Additionally, the energy management system 106 may further monitor the PMV value of Zone 1 in real time to further optimize the energy utilized in the said zone by increasing the temperature set point or reducing the VAV flow rate. In one example, when the value of PMV lies in the range of −1 to +1, the system performs further optimization of the HVAC parameters to facilitate higher energy saving.

The following example is only to elucidate principles of the present subject matter and is not to be construed as a limitation.

For example, while training the analyzing module, the data associated with the following scenarios may be provided. In an instance, different scenarios for Zone 1 of the facility 500 may be considered, where Zone 1 is a lobby area with a design occupancy of 10 occupants. The design occupancy indicates the maximum number of occupants for which a temperature set point or a VAV flow rate of the zone is initially configured to. In one example, at 9:00 am, the zone temperature set point may be considered to be 24 degrees C (optimum temperature set point for 10 occupants), the VAV flow rate may considered to be ‘x’ CFM, the occupancy count may be 10, and the PMV may be measured to be between +1 to −1. In another example, at 12:00 pm, the zone temperature set point may be considered to be 24 degrees C, the VAV flow rate may considered to be ‘x’ CFM, the occupancy count may vary between 8-10, and the PMV may be measured to be between +1 to −1.

Accordingly based on this data, the analyzing module 504 may transition the temperature set point of 24 degrees C. to 25 degrees by increasing the temperature set point, when a decrease in the occupancy is monitored and accordingly, the VAV flow rate of the zone may be reduced by 100 CFM.

On deployment of the energy management system 106, the energy management systems 106 may obtain one or more parameters associated with Zone 1, in real time. For example, at 5 pm, the current zone temperature set point of the zone may be 24 degrees C., the current VAV flow rate of the zone may be ‘x’ CFM, the occupancy may vary from 10 to 2, and the PMV may be measured to be between +1 to −1. The system 106, in one example, based on the one or more parameters obtained and historical transition data from the previous two scenarios observed at 9 am and 12 pm, may determine the optimum temperature set point and the optimum VAV flow rate. That is, based on the transitioning of the temperature set point and the VAV flow rate from occupancy of 10 to occupancy of 8, the system may proportionally determine the optimum temperature set point and VAV flow rate for the zone for 2 occupants, for example, the temperature set point may be increase by 3 points and the VAV flow rate may be decreased by a factor of 3.

Additionally, in one example, the one or more parameters of the zone may include a zone recovery time. A zone recovery time is the time taken for transitioning the current temperature set point to the optimum temperature set point or for transitioning a current VAV flow rate to an optimum VAV flow rate. In one example, the system 106 may assign a priority for each zone based on the zone recovery time of the said zone. For example, there may be a zone in the facility which is constantly exposed to sunlight, and the time taken for transitioning of the temperature set point from the current temperature set point to the optimum temperature set point may be very high when compared to a zone which receives minimal or almost no sunlight. Therefore, zones with longer recovery times, such as those with high sunlight exposure, may be assigned lower priority to optimize energy conservation efforts in an efficient manner.

Therefore, when determining the optimum temperature set points and VAV flow rates, techniques of the present subject matter consider not only the immediate energy savings but also the energy expenditure required during the transition period. By factoring in the zone recovery time, the energy management system can make efficient optimizations, potentially choosing to make smaller, more frequent adjustments in zones with longer recovery times to balance comfort and energy efficiency. This comprehensive approach thus ensures that the energy optimization strategy accounts for both short-term adjustments and long-term energy conservation objective across all zones in the facility.

FIG. 6 illustrates an example of energy optimization in a facility 600, in accordance with an example implementation of the present subject matter. In one example, facility 600 may be an office building with zone-1 602 an open office space which includes multiple workstations. In one example, details associated with zone-1 602 of the facility 600 may be obtained by the facility management system 604. The facility management system 604 may communicate the data obtained from Zone-1 with the energy management system 106. In one example, the energy management system 106 may be a part of the facility management system 604. In another example, the energy management system 106 may be external to the facility management system and may be hosted on the server and accessible to the facility management system.

Further, three instances of zone-1 may be considered at different time periods. A first instance 606 at 9 am in the morning, a second instance 608 at 1 pm in the afternoon, and a third instance 610 at 6 pm in the evening may be considered. In one example, to optimize the energy utilization of zone-1 throughout the day, one or more parameters associated with zone-1 602 of the facility 600 may be obtained by the energy management system 106 in real-time, or for example, every 15 minutes.

In one example, Zone-1 may have a designed occupancy of 10 occupants, for which the optimum temperature set point would be 21 degrees C. In the first instance 606, that is at 9 am, the office space is fully occupied with all 10 occupants present. Based on the number of occupants present, the occupancy percentage at 9 am would be 100%. Referring to the states and occupancy percentages described with reference to FIGS. 3 and 4, the state of occupancy may be detected as state ‘F’. Therefore, the optimum temperature set point for state ‘F’ would be set to 21 degrees C., which is deemed to be comfortable for a fully occupied space.

In the second instance 608, the office space is at 20% occupancy, with 2 employees present, as others are out for lunch, meetings, or working remotely. Accordingly, the occupancy percentage has dropped from 100% to lying between 1-20%. The state corresponding to the occupancy percentage of 20% would be detected as state ‘A’. Therefore, to conserve energy, the energy management system 106 may regulate the temperature set point to 24 degrees Celsius from 21 degrees C., which is still comfortable for the reduced number of occupants but uses less energy than maintaining the full occupancy temperature.

In each scenario, the energy management system 106 uses occupancy data to determine the current state of occupancy and determines the optimum temperature set point for the zone. Additionally, in one example, the energy management system 106 may control a VAV flow rate of the zone in correspondence to the optimum temperature set point determined. In another example, the energy management system may control a supply air rate of an AHU supplying to the zone in correspondence to the optimum temperature set point determined. Therefore, techniques of the present subject matter ensure that the office space is not over-conditioned when few people are present, leading to energy savings, while still maintaining a comfortable environment when occupancy levels are higher. The system may also incrementally adjust the temperature in steps to avoid sudden changes that could affect comfort levels.

FIG. 7 illustrates another example for energy optimization in a facility 700, in accordance with an example implementation of the present subject matter. The following example illustrates a facility 700, an office building, which includes three zones, Zone A, Zone B, and Zone C and is not to be construed as a limitation. In one example, Zone A may be a workspace, Zone B may be a meeting room, and Zone C may be a recreational area.

To optimize the energy utilized in facility 700, the energy management system 106 may monitor one or more parameters associated with Zone A, Zone B, and Zone C respectively. In one example, details associated with Zone A, Zone B, and Zone C of the facility 700 may be obtained by the facility management system 702. The facility management system 702 may communicate the data obtained from Zone A, Zone B, and Zone C with the energy management system 106. In one example, the energy management system 106 may be a part of the facility management system 702. In another example, the energy management system 106 may be external to the facility management system 702 and may be hosted on the server and accessible to the facility management system 702.

In one example, the energy management system 106 may assign a priority to each zone. In one example, the one or more zones of the facility may be assigned priority based on the zone recovery time. In this example, Zone A and Zone B may be adjacent to one another and may be located on the first floor of the facility 700. Zone A and Zone B may be located in the central region of the facility 700 with higher levels of insulation and may be equipped with one or two windows, where the exposure to natural light for prolonged periods may be limited. On the other hand, Zone C may be located at a boundary region on the top floor of the facility 700, where Zone C may have a full be equipped with a full-length glass wall through which the zone may receive prolonged periods of direct sunlight and also may receive additional heat from the roof of the facility owing to its location in the facility 700.

Due to such factors, such as exposure to natural light, level of insulation, number of windows and doors, location of the zone, layout of the zone, and the like, the zone recovery time for each of these zones may vary with respect to one another. In this example, since Zone 3 is exposed to prolonged periods of sunlight and additional heat radiating from the roof, and also heat generated from the equipment installed in the said zone, the time required to transition a temperature set point or a VAV flow rate of the zone to its optimum value at a given point of time would be higher than that required for Zone A and Zone B. Accordingly, Zone C may be assigned the lowest priority and Zones A and B may be assigned a higher priority. In one example, although Zone A and Zone B are adjacent to one another with similar structural features, Zone A may be assigned a higher priority than Zone B, since Zone B may be less frequently used when compared to Zone A.

On assigning a priority index to the one or more zones, and based on the occupancy data, occupancy schedules, and details corresponding to the facility, the energy management system 106 may determine an optimum temperature set point and an optimum VAV flow rate each of the zones, Zone A, Zone B, and Zone C.

For example, in Zone A, at 9 am, the temperature set point may be set to 21 degrees Celsius with a high VAV flow rate to accommodate all employees. At 2 pm, with reduced occupancy, the temperature set point may be increased to 24 degrees Celsius, and the VAV flow rate is decreased to conserve energy. At 5 pm, the system 106 may adjust the temperature set point of 22 degrees Celsius with a VAV flow rate that balances comfort and energy efficiency for the 70% occupancy level. In one example, in Zone B, the temperature set point and VAV flow rate may be determined based on an occupancy schedule in addition to the occupancy level being monitored dynamically. For example, if there is a meeting scheduled from 2:00 pm to 4:00 pm, the temperature set point for Zone B may be set to 23 degrees C from 1:30 to 4:30 pm, or the like with a lower VAV flow rate. Whereas in Zone C, the temperature set point may be maintained at a comfortable 22 degrees Celsius with a moderate VAV flow rate to ensure a stable environment instead of constantly regulating the temperature set points or the VAV flow rate owing to the high zone recovery time, thereby ensuring the overall energy optimization of the facility is dynamic, fast, and efficient, maintaining the indoor air quality and thermal comfort.

Further, techniques of the present subject matter may control the VAV flow rates of multiple zones simultaneously. For example, Zone A and Zone B of the facility 700 may be supplied air through a first VAV box 704 and Zone C may be supplied air though a second VAV box 706. The first VAV box 704 connected to both zones, Zone A and Zone B, may be equipped with controllable dampers for each zone. These dampers may adjust the volume of air delivered to each zone independently. For example, in the meeting room (Zone B), occupancy sensors may detect the presence of people and signal the VAV box to open the damper for that zone. Alternatively, if a meeting is scheduled, the system can preemptively adjust the temperature set points and the VAV flow rates to the desired set point before occupants arrive.

The energy management system 106 may be configured to control the first VAV box 704 to optimize airflow based on the occupancy status of both zones. That is, if both zones (Zone A and Zone B) are occupied, the first VAV box 704 may be configured to increase its overall airflow to meet the demand while still controlling the individual dampers to maintain zone-specific temperature set points. Alternatively, if one zone is unoccupied while the other is in use, the first VAV box 704 may be configured to reduce its overall airflow to save energy while directing the majority of the airflow to the occupied zone. In one example, the energy management system 106 may also consider all the thermal loads present in each of these zones, which may differ based on factors such as the number of occupants, equipment heat gain, solar load, and the like to adjust the airflow to maintain comfort in both zones without over-conditioning the space.

Therefore, by utilizing a single VAV box, such as the first VAV box 704, with independently controlled dampers that responds to real-time occupancy data, it is possible to efficiently manage the climate of two zones with different occupancy levels, thereby allowing higher energy savings by reducing the airflow to unoccupied areas while ensuring that occupied spaces remain comfortable.

FIG. 8 illustrates a method 800 for energy optimization, in accordance with an example implementation of the present subject matter. The order in which the method 800 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement method 800 or an alternative method. Additionally, individual blocks may be deleted from the method 800 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 800 may be implemented in any suitable hardware, computer readable instructions, firmware, or combination thereof. For discussion, the method 800 is described with reference to the implementations illustrated in FIG(S). 1-7.

At block 802, the method 800 includes obtaining, by the energy management system, one or more parameters corresponding to a zone from amongst a plurality of zones within the facility. The one or more parameters include occupancy data, facility data, and an occupancy schedule, where the occupancy data indicates a number of occupants present in the zone at a first instance of time. In one example, the occupancy data corresponding to each zone of the facility is obtained at pre-determined time intervals. Further, in one example, the one or more parameters include a zone recovery time, where the zone recovery time corresponds to the time taken for transitioning the current temperature set point of the zone to the optimum temperature set point. In one example, a priority may be assigned to each zone of the facility in correspondence to the zone recovery time.

At block 804, the method 800 includes determining, by the energy management system, an optimum temperature set point of the zone in correspondence to the one or more parameters.

In one example, the optimum temperature set point may be determined based on a state of occupancy detected in the zone. In one example, the energy management system may detect the state of occupancy from amongst a plurality of states of occupancy, where the state of occupancy is detected in correspondence to the occupancy data. On detecting the state of occupancy, a transition value to transition the current temperature set point of the zone to the optimum temperature set point of the zone may be determined, where the transition value is determined based on a pre-determined threshold value associated with each state of occupancy from amongst the plurality of states of occupancy.

In one example, the optimum temperature set point is determined in correspondence to an occupancy schedule predicted for the zone and the current temperature set point of the zone is transitioned to the optimum temperature set point of the zone based on the predicted occupancy schedule.

In one example, the optimum temperature set point of the zone may be determined based on analyzing historical trends associated with the one or more HVAC parameters of the zone in addition to the one or more parameters such as location data, structural design data, data associated with external environmental factors influencing the zone, the occupancy data, historical occupancy data, historical occupancy patterns for a zone, occupancy schedules, and the like.

In one example, a Variable Air Volume (VAV) flow rate of the zone may be controller in correspondence to the optimum temperature set point. Also, in a scenario where the VAV flow rate is unavailable, an Air Handling Unit (AHU) supply air flow rate of the zone may be controlled in correspondence to the optimum temperature set point.

Further in one example, the energy management system may determine an optimum Variable Air Volume (VAV) flow rate of the zone in correspondence to the one or more parameters. In one example, at least a first optimum VAV flow rate for a first zone amongst the plurality of zones within the facility and a second optimum VAV flow rate for a second zone amongst the plurality of zones within the facility is determined, where the first zone and the second zone are coupled to at least one VAV box.

At block 806, the method 800 includes transitioning, by the energy management system, a current temperature set point of the zone to the optimum temperature set point of the zone, where the transitioning to the optimum temperature set point of the zone is to optimize energy utilization of the zone. In one example, the occupancy data may be monitored for a first time period to transition the current temperature set point of the zone to the optimum temperature set point of the zone. In one example, transitioning the current temperature set point of the zone to the optimum temperature set point further comprises monitoring, by the energy management system, a predicted mean vote of the zone to ensure thermal comfort, where the predicted mean vote of the zone is computed in correspondence to an air temperature of the zone, a mean radiant temperature of the zone, air velocity of the zone, relative humidity of the zone, activity level in the zone, and clothing insulation.

Further, in one example, the method 800 includes analyzing the one or more parameters and the current temperature set point of the zone at pre-defined time intervals to identify a new optimum temperature set point of the zone and transition the current temperature set point of the zone to the new optimum temperature set point of the zone. Similarly, in one example, the method 800 includes analyzing the one or more parameters and the current VAV flow rate of the zone at pre-defined time intervals to identify a new optimum VAV flow rate of the zone and transition the current VAV flow rate of the zone to the optimum VAV flow rate of the zone.

FIG. 9 illustrates a method for transitioning a temperature set point for a zone, in accordance with an example implementation of the present subject matter. The order in which the method 900 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement method 900 or an alternative method. Additionally, individual blocks may be deleted from the method 900 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 900 may be implemented in any suitable hardware, computer readable instructions, firmware, or combination thereof. For discussion, the method 900 is described with reference to the implementations illustrated in FIG(S). 1-7.

At block 902, the method 900 includes obtaining one or more parameters corresponding to a zone from amongst a plurality of zones within the facility. The one or more parameters include occupancy data, facility data, and an occupancy schedule, where the occupancy data indicates a number of occupants present in the zone at a first instance of time. In one example, the occupancy data corresponding to each zone of the facility is obtained at pre-determined time intervals.

At block 904, the method 900 includes computing an occupancy percentage of the zone at the first instance of time based on the occupancy data in real-time. In one example, the state of occupancy from amongst a plurality of states of occupancy may be detected. In another example, the occupancy percentage for a zone may be predicted based on the occupancy schedule obtained for the zone.

At block 906, the method 900 includes detecting a state of occupancy. In one example, the state of occupancy may be detected based on the occupancy percentage computed for the zone. In another example, the state of occupancy may be detected based on the occupancy percentage predicted for the zone.

At block 908, the method 900 includes determining a transition value to transition the current temperature set point of the zone to the optimum temperature set point of the zone. In one example, the transition value is determined based on a pre-determined threshold value associated with each state of occupancy from amongst the plurality of states of occupancy.

At block 910, the method 900 includes monitoring the occupancy data for a first time period. In one example, the occupancy data of the zone is monitored for a first time period to transition the current temperature set point of the zone to the optimum temperature set point of the zone. For example, the first time period may be 15 minutes, or 10 minutes, and the like. That is, if it is observed that the occupancy data is constant for 15 minutes, at step 912, the method 900 includes determining if the PMV criteria for the zone is satisfied. In one example, if it is determined that the PMV criteria lies between −1 to +1, at block 918, the current temperature set point of the zone is transitioned to the optimum temperature set point based on the transition value. However, if the occupancy data is not constant for the first time period, then at block 914, the method 900 maintains the current temperature set point for the zone. Similarly, if the PMV value determined for the zone is beyond the range of −1 to +1, at block 916, the method 900 maintains the current temperature set point for the zone.

FIG. 10 illustrates another example method for energy optimization, in accordance with an example implementation of the present subject matter. The order in which the method 1000 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement method 1000 or an alternative method. Additionally, individual blocks may be deleted from the method 1000 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 1000 may be implemented in any suitable hardware, computer readable instructions, firmware, or combination thereof. For discussion, the method 1000 is described with reference to the implementations illustrated in FIG(S). 1-7.

At block 1002, the method 1000 includes obtaining one or more parameters corresponding to a zone from amongst a plurality of zones within the facility, where the one or more parameters include occupancy data, facility data, occupancy schedule, and a zone recovery time. The occupancy data indicates a number of occupants present in the zone at a first instance of time and the zone recovery time corresponds to the time taken for transitioning at least one of the current temperature set point of the zone to the optimum temperature set point of the zone and the current VAV flow rate of the zone to the optimum VAV flow rate of the zone.

At block 1004, the method 1000 includes determining at least one of an optimum temperature set point of the zone and an optimum VAV flow rate of the zone in correspondence to the one or more parameters.

At block 1006, the method 1000 includes transitioning a current temperature set point of the zone to the optimum temperature set point of the zone and a current VAV flow rate of the zone to the optimum VAV flow rate of the zone, where the transitioning to the optimum temperature set point of the zone and transitioning optimum VAV flow rate of the zone is to optimize energy utilization of the zone.

FIG. 11 illustrates a non-transitory computer-readable medium for energy optimization in a facility, in accordance with an example implementation of the present subject matter. In an example, the computing environment 1100 includes processor 1102 communicatively coupled to a non-transitory computer readable medium 1104 through communication link 1106. In an example implementation, the computing environment 1100 may be for example, the system for energy management, system 106. In an example, the processor 1102 may have one or more processing resources for fetching and executing computer-readable instructions from the non-transitory computer readable medium 1104. The processor 1102 and the non-transitory computer readable medium 1104 may be implemented, for example, in the system for energy management.

The non-transitory computer readable medium 1104 may be, for example, an internal memory device or an external memory. In an example implementation, the communication link 1106 may be a network communication link, or other communication links, such as a PCI (Peripheral component interconnect) Express, USB-C (Universal Serial Bus Type-C) interfaces, I2C (Inter-Integrated Circuit) interfaces, and the like. In an example implementation, the non-transitory computer readable medium 1104 includes a set of computer readable instructions 1110 which may be accessed by the processor 1102 through the communication link 1106 and subsequently executed for energy management. The processor(s) 1102 and the non-transitory computer readable medium 1104 may also be communicatively coupled to a computing device 1108 over the network.

Referring to FIG. 11, in an example, the non-transitory computer readable medium 1104 includes computer readable instructions 1110 that cause the processor 1102 to obtain one or more parameters corresponding to a zone from amongst a plurality of zones within the facility, where the one or more parameters include occupancy data, facility data, and an occupancy schedule, where the occupancy data indicates a number of occupants present in the zone at a first instance of time. The instructions 1110 may further cause the processor 1102 to determine an optimum temperature set point of the zone and an optimum VAV flow rate of the zone in correspondence to the one or more parameters. Further, the instructions 1110 may cause the processor 1102 to transition a current temperature set point of the zone to the optimum temperature set point of the zone and a current VAV flow rate of the zone to the optimum VAV flow rate of the zone, wherein the transitioning to the optimum temperature set point of the zone and transitioning optimum VAV flow rate of the zone is to optimize energy utilization of the zone.

In one example, the instructions 1110 may further cause the processor 1102 to analyse the one or more parameters, a current temperature set point of the zone, and a current VAV flow rate of the zone at predefined time intervals to transition the current temperature set point of the zone and the current VAV flow rate of the zone to the optimum temperature set point of the zone and the optimum VAV flow rate, respectively. Further, in one example, the one or more parameters may include a zone recovery time, where the zone recovery time corresponds to the time taken for transitioning at least one of the current temperature set point of the zone to the optimum temperature set point of the zone and the current VAV flow rate of the zone to the optimum VAV flow rate of the zone.

Although examples of the present subject matter have been described in language specific to methods and/or structural features, it is to be understood that the present subject matter is not limited to the specific methods or features described. Rather, the methods and specific features are disclosed and explained as examples of the present subject matter.