Determining recovery times for comfort conditions

Abstract

Systems, methods, and / or media determine a recovery time for a comfort condition. Area temperatures (418) of a plurality of areas of a facility (404) are received. A heat transfer characteristic is identified between the first region and a second region, where the second region is positioned adjacent to the first region. An initiation time (422) for a zone temperature action of the first zone is determined based on a first zone temperature of the first zone, a second zone temperature of the second zone, and a heat transfer characteristic. A zone command (420) is sent to a temperature control system of the facility (404), where the zone command (420) includes an initiation time (422) of a zone temperature action for the first zone.

Description

Technical Field

[0001] This application relates to the field of building management systems, and more specifically, to the management of comfort conditions in multi-room buildings. Background Technology

[0002] Building management systems encompass a wide variety of systems that help monitor and control various aspects of building operations, specifically environmental or comfort conditions such as heating, ventilation, and air conditioning (“HVAC”). Components of a building management system can be widely distributed throughout the facility or campus. For example, a system may include sensors, actuators, controllers, and control stations to manage HVAC systems with heaters, coolers, fans, ductwork, pipes, dampers, and valves located within the building. Different areas of a building may have different environmental settings based on usage and the individual preferences of the occupants.

[0003] In buildings, energy is saved by allowing spaces to deviate from a comfortable temperature range when not in use. When people begin to occupy the space again, the temperature needs time to return to a comfortable level—that is, preheating or cooling. The time required to restore comfortable conditions is estimated based on the space temperature, the external building temperature, and the space's thermal characteristics. This method ignores the influence of other temperatures related to managing comfort conditions, such as other environmental conditions within the building. Therefore, the lack of precision in the recovery time makes the preheating / cooling operation of the space inefficient.

[0004] Traditional building management systems attempt to generate comfortable conditions but still lack optimal precision. Nighttime reheating features allow buildings to cool during periods of no use to save energy. Preheating features restore a building that has been allowed to cool to the conditions required during occupancy. Optimal start-up features initiate the preheating process as late as possible while reaching the occupancy temperature required for the predetermined occupancy time in a timely manner. Optimal start-up considers both the heating system and quantitative aspects of the thermal processes that warm the space. Adaptive optimal start-up features observe the performance of the preheating process over time and automatically adjust parameters based on observed performance to improve future performance.

[0005] In traditional systems, environmental data corresponding to the connected space is grouped together; that is, various thermal energy storage levels are merged into one. For example, the thermal state can be represented by a single temperature value. This simplistic approach does not optimize recovery time for more refined comfort conditions. Summary of the Invention

[0006] According to one embodiment of this disclosure, a connected space method is provided for determining the recovery time of comfort conditions in a building management system. The method considers heat transfer between connected spaces to determine the appropriate time to restore comfort conditions in a region. The method can be analytical or uses machine learning to effectively account for heat transfer between connected spaces to calculate a set of individual start-up times for each region.

[0007] One aspect is a building management system for determining the recovery time of comfort conditions, comprising: an input component; a processor, directly or indirectly coupled to the input component; and an output component, directly or indirectly coupled to the processor. The input component receives multiple zone temperatures of multiple zones within a facility, wherein the zones include a first zone and a second zone adjacent to the first zone. The processor identifies heat transfer characteristics between the first and second zones. The processor determines the activation time of a zone temperature action in the first zone based on the first zone temperature, the second zone temperature, and the heat transfer characteristics. The output component sends a zone command to the facility's temperature control system, wherein the zone command includes the activation time of the zone temperature action in the first zone.

[0008] On the other hand, there is a method for determining the recovery time of comfort conditions. This involves receiving zone temperatures of multiple zones within a facility, wherein the zones include a first zone and a second zone adjacent to the first zone. The heat transfer characteristics between the first and second zones are identified. Based on the first zone temperature of the first zone, the second zone temperature of the second zone, and the heat transfer characteristics, the activation time of the zone temperature action in the first zone is determined. A zone command is sent to the facility's temperature control system, wherein the zone command includes the activation time of the zone temperature action in the first zone.

[0009] Another aspect is a building management system for determining the recovery time of comfort conditions, comprising: an input component; a processor, directly or indirectly coupled to the input component; and an output component, directly or indirectly coupled to the processor. The input component receives multiple zone temperatures from multiple zones of the facility, wherein the zones include a first zone and a second zone adjacent to the first zone. The processor determines the activation time of the zone temperature action of the first zone based on the first zone temperature of the first zone, the second zone temperature of the second zone, and the heat transfer characteristics between the first and second zones. The output component sends a zone command to the facility's temperature control system, wherein the zone command includes the activation time of the zone temperature action of the first zone.

[0010] Another approach is a method for determining the recovery time of comfort conditions. It receives zone temperatures from multiple zones of a facility, wherein the zones include a first zone and a second zone adjacent to the first zone. The activation time of the zone temperature action in the first zone is determined based on the first zone temperature, the second zone temperature in the second zone, and the heat transfer characteristics between the first and second zones. A zone command is sent to the facility's temperature control system, wherein the zone command includes the activation time of the zone temperature action in the first zone.

[0011] The foregoing features and advantages, as well as other features and advantages, will become more apparent to those skilled in the art upon reference to the following detailed description and accompanying drawings. While it is intended to provide one or more of these or other advantageous features, the teachings disclosed herein extend to embodiments that fall within the scope of the appended claims, regardless of whether they achieve one or more of the foregoing advantages. Attached Figure Description

[0012] To gain a more complete understanding of this disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, wherein the same numerals denote the same objects.

[0013] Figure 1 A system diagram is shown illustrating a building management system operable in an example implementation to employ the technologies described herein.

[0014] Figure 2 Show Figure 1 A block diagram of an example management device used to determine the recovery time for comfort conditions.

[0015] Figure 3 The illustration shows a building environment operable in an example implementation to employ techniques for determining recovery time for comfort conditions.

[0016] Figure 4 A data flow diagram of a building management system operable in an example implementation to employ the techniques described herein is shown.

[0017] Figure 5 A sequence diagram of an example operation for determining the recovery time for comfort conditions is shown.

[0018] Figure 6 A sequence diagram of another example operation for determining the recovery time of comfort conditions is shown.

[0019] Figure 7 A separate connection procedure is shown as yet another example operation for determining the recovery time of comfort conditions. Detailed Implementation

[0020] Various techniques relating to systems and methods that facilitate the determination of recovery time for comfort conditions will now be described with reference to the accompanying drawings, wherein the same reference numerals consistently denote the same elements. The drawings discussed below, as well as the various embodiments used in this patent document to describe the principles of this disclosure, are merely illustrative and should not be construed as limiting the scope of this disclosure in any way. Those skilled in the art will understand that the principles of this disclosure can be implemented in any suitably arranged apparatus. It will be understood that functionality described as being performed by certain system elements can be performed by multiple elements. Similarly, for example, elements can be configured to perform functionality described as being performed by multiple elements. Numerous innovative teachings of this application will be described with reference to exemplary, non-limiting embodiments.

[0021] refer to Figure 1 This diagram illustrates a block diagram of a building management system (“BMS” or “system”) 100 in an example implementation for an environment. System 100 includes one or more network connections or main buses 102 for connecting components to a management-level network (“MLN”) of system 100. In one implementation, example system 100 may include one or more management-level devices or management devices, such as management workstation 104, management server 106, or remote management device 108 connected via wired or wireless network 110, which allows setting and / or changing various controls of the system. Management devices may also be portable management devices connected to individual automation or field-level devices of system 100 via wired or wireless links. While a brief description of system 100 is provided below, it should be understood that the system described herein is merely an example of a particular form or configuration of the system. System 100 may be implemented in any other suitable manner without departing from the scope of this disclosure. The management device is configured to provide overall control and monitoring of the automation devices, field devices, and other devices of system 100.

[0022] One or more management devices of system 100 include one or more processors 112, one or more input components 114, and one or more output components 116. These components of the one or more management devices allow for the determination of the recovery time of the comfort conditions of system 100.

[0023] for Figure 1In the illustrated implementation, system 100 provides connectivity for subsystems of various building parameters, such as components of environmental comfort, fire safety, and security systems, based on one or more communication protocols. Each subsystem 118, 120 may include various types of automation controllers and field devices 124, 126 (“automation controllers”) for monitoring and controlling areas within a building or building complex. Examples of automation controllers and field devices 124, 126 include, but are not limited to, actuators, field panels, sensors, third-party devices, etc. These automation controllers and field devices 124, 126 may communicate via one or more communication protocols, such as BACnet, KNX, LonWorks, Modbus, etc.

[0024] refer to Figure 2 Example components 200 of management devices 104, 106, and 108 for determining recovery time for comfort conditions are shown. Device components 200 include one or more communication lines 202 for directly or indirectly interconnecting other device components. Other device components include one or more communication components 204, one or more processors 206, and one or more memory components 208 for communicating with other entities via wired or wireless networks. Communication components 204 transmit (i.e., receive and / or send) data associated with one or more devices of system 100 and their associated devices (such as controllers and devices 124, 126). Communication components 204 can communicate using wired or wireless technologies. Examples of wireless communication technologies include, but are not limited to, Bluetooth (including BLE), Ultra Wideband (UWB), Wi-Fi (including Wi-Fi Direct), Zigbee, cellular networks, mesh networks, PAN, WPAN, WAN, near-field communication, and other types of radio communication and variations thereof.

[0025] One or more processors 206 can send data to other components of device component 200 and process commands received from other components, such as information from communication component 204 or memory component 208. Each application includes executable code for providing specific functionality for processor 206 and / or managing the remaining components of devices 104, 106, 108. Examples of applications executable by processor 206 include, but are not limited to, identification / determination module 210 and optimizer module 212. Identification / determination module 210 of processor 206 identifies or determines the heat transfer characteristics between adjacent region pairs. Identification / determination module 210 also determines the initiation time of a region temperature action for a particular region based on the temperature of a particular region, the temperatures of one or more regions adjacent to the particular region, and the heat transfer characteristics of each pair of regions. Optimizer module 212 of processor 206 may include statistical models to analyze patterns in data and draw inferences therefrom. Examples of techniques used by optimizer module 212 include, but are not limited to, random forests of artificial neural networks or decision trees.

[0026] The data stored in memory component 208 is information that can be referenced and / or manipulated by modules of processor 206 for performing the functions of management devices 104, 106, and 108. Examples of data associated with management devices 104, 106, and 108 and stored by memory component 208 may include, but are not limited to, time-series data of zone temperatures, heat transfer characteristics 214, and timing / command data 216. Heat transfer characteristics 214 are identified between adjacent zone pairs and are related to the effect of adjacent heat on a particular zone. Timing / command data 216 includes the start time of zone temperature action and zone commands to the temperature control system of the facility. The start time is determined based on a particular zone, one or more zones adjacent to the particular zone, and the heat transfer characteristics associated with each pair of zones. Zone commands include the start time of zone temperature action for a particular zone.

[0027] Device component 200 may include an input component 218 that manages one or more input components, and / or an output component 220 that manages one or more output components. The input component 218 and output component 220 of device component 200 may include one or more visual components, audio components, mechanical components, and / or other components. In some implementations, the input component 318 and output component 320 may include a user interface 322 for user interaction with the device. The user interface 322 may include a combination of hardware and software to provide the user with a desired user experience.

[0028] It should be understood that Figure 2Provided for illustrative purposes only to show example implementations of management devices 104, 106, and 108, and not intended to be a complete schematic diagram of the various components that may be used with such devices. Management devices 104, 106, and 108 may include Figure 2 Various other components, not shown, may include combinations of two or more components, or may divide a particular component into two or more separate components, and are still within the scope of this invention. Furthermore, components 200 may be directly or indirectly coupled to each other to perform operations of management devices 104, 106, 108. For example, processor 206 may be directly or indirectly coupled to input component 218. Similarly, output component 220 may be directly or indirectly coupled to processor 206.

[0029] refer to Figure 3 The diagram illustrates a built environment 300 in an example implementation, which can be operated to employ techniques for determining recovery times for comfort conditions. For the built environment 300, system 100 controls the environment, such as temperature, in multiple zones 302 of the facility. For example, system 100 can individually control the temperature of each of the multiple zones 302. The system can optimize the start-up time of zone temperature actions, regulating warming or cooling to balance energy consumption and occupant comfort.

[0030] like Figure 3 As shown, the system can modify the activation time of zone temperature actions across an entire floor on a zone-by-zone basis. During system operation, the temperature in each zone 302 can allow for cooling and preheating to optimize comfort conditions. For each zone 302, system 100 can determine the activation time for regulating the zone temperature, such as activating heating or cooling of the zone, based on multiple zone temperatures and one or more heat transfer characteristics 304. Zone temperatures can be measured by temperature sensors of the zone and may include zone profiles to regulate zone temperatures based on zone characteristics. Examples of heat transfer characteristics include, but are not limited to, zone thermal mass, thermal contact between the zone and the outside, the effects of the heating / cooling system, and contact with other zones. Zone thermal mass can indicate the heat retention of a particular zone and its sensitivity to temperature fluctuations. Thermal contact between the zone and the outside 306 indicates the effect of temperature differences relative to areas outside the facility. The effects of the heating / cooling system 308 (such as zone thermal contact with other zones) indicate the effect of temperature differences relative to adjacent zones 302 within the facility. In some implementations, system 100 may determine the start time of the zone temperature action for each zone 302 (such as the first zone) based on the first zone temperature of a specific zone, the second zone temperature of another zone (such as an adjacent zone), and one or more heat transfer characteristics associated with these zones.

[0031] It should be understood that, despite Figure 3Some arrows indicate heat transfer from the inner region to each region and heat transfer from one region to the outer region, but heat can be transferred in other directions and / or in both directions.

[0032] refer to Figure 4 This diagram illustrates a data flow diagram of a building management system 100 operable in an example implementation to employ the techniques described herein. The environment 400 of the building management system 100 includes a building automation system 402, facilities 404, and an optimizer 406. The environment 400 may also include user equipment 408, which represents input provided by operators or occupants of the building 404. Although in Figure 4 Although they are shown as separate entities, the various components 402, 406, and 408 of system 100 may be located within facility 404 or outside the facility boundary.

[0033] In some implementations, optimizer 406 is trained to identify heat transfer characteristics by analyzing a first set of conditions: a first region temperature, a second region temperature, an external temperature, and known start / stop conditions. In some implementations, the trained optimizer 406 is operated to indicate start and / or stop signals. Start / stop signals may be based at least in part on a second set of conditions: the first region temperature, the second region temperature, and the heat transfer characteristics. Examples of start signals are activation signals for heating and / or cooling, and examples of stop signals are wait / delay signals. In such implementations, optimizer 406 may be based on a machine learning model. Examples of optimizers 406 include, but are not limited to, artificial neural networks, random forests of decision trees, etc.

[0034] For the machine learning-based optimizer 406, the model can be replicated to represent a building or part of a building with multiple individual control spaces. For example, the replicated model can support preheating optimized individually for each zone. Traditional systems fail to optimize individual spaces by neglecting heat transfer between rooms, which has a significant impact on the temperature trends of each room. Systems 100 and 402 represent more realistic models that can be implemented in the cloud. Optimizer 406 of systems 100 and 402 determines the start time of zone temperature actions for each zone. Examples of zone temperature actions include, but are not limited to, heating activation, cooling activation, etc. Examples of start times include, but are not limited to, the latest start time for each zone, the start time for heating a zone using the least energy, and / or the start time with the most favorable utility load curve. Optimizer 406 can be trained using reinforcement learning methods to understand the specific building 404, thereby defining performance functions, balancing energy use, and achieving reliable comfort outcomes. Optimizer 406 observes the periodic operations (such as daily) of the entire zone set and learns the thermal characteristics of heat transfer between control zones. It uses time-related data from each zone to learn how they influence each other. This allows for accurate calculation of the startup time for each region.

[0035] like Figure 4 As shown, the five inputs 410 of optimizer 406 are similar to the five inputs 412, 414, 416, 418, and 420 of building automation system 402. Building automation system 402 receives occupant-centric control schedule signals 412 and temperature setpoint signals 414 from user equipment 408, which change infrequently. Building automation system 402 also receives external temperature signals 416, zone temperature signals 418 for each zone, and zone HVAC status 420 for each zone from facility 404. These building signals 416, 418, and 420 may be time-varying signals that are periodically updated. Optimizer 406 receives some or all of these inputs 412, 414, 416, 418, and 420 as its own input 410 to building automation system 402, and the optimizer may process time-series data or current values. Optimizer 406 provides signals 422 to system 402, wherein one or more signals represent a data point for each zone. In response to these optimizer inputs 410, optimizer 406 generates a start signal or a non-start signal, one signal 422 for each zone, such as start now or wait. Optimizer 406 provides the start / non-start signal 422 to building automation system 402, and examples of signal 422 include, but are not limited to, the expected time required to start the zone or the expected duration of a preheating (or cooling) process. Building automation system 402 sends zone commands 424 to the temperature control system of facility 404, wherein the zone command includes the start time of the zone temperature action for each zone, such as a zone HVAC command indicating the start time.

[0036] refer to Figure 5 , Figure 5 A sequence diagram of an example operation 500 for determining the recovery time for comfort conditions is shown. System 100 receives (502) data associated with multiple areas of the facility. For example, the system may receive (504) the area temperature of the areas, where each area temperature corresponds to a specific area. In some embodiments, system 100 receives (506) the external temperature of the external areas of the facility and the area temperature of the areas within the facility.

[0037] In response to receiving (502) data from multiple zones, system 100 identifies (508) the heat transfer characteristics between adjacent zone pairs of the facility. For example, one of the heat transfer characteristics may be associated with a first zone and a second zone, wherein the second zone is located adjacent to the first zone. In some implementations, system 100 may determine (510) the heat transfer characteristics between the first zone and all zones adjacent to the first zone. In some implementations, system 100 may identify (512) the heat transfer characteristics of insulators and non-insulators shared with the first and second zones. Examples of insulators include, but are not limited to, walls, floors, ceilings, partitions, and enclosed entrances / exits to / from each zone. Examples of non-insulators include, but are not limited to, vents, passageways, open entrances / exits, and other openings to / from each zone.

[0038] After identifying (508) the heat transfer characteristics of adjacent areas and / or in response to receiving (502) data from multiple areas, system 100 determines (516) the initiation time of the area temperature action for each area. For example, system 100 may determine the initiation time of the first area based on the first area temperature of the first area, the second area temperature of the second area, and the heat transfer characteristics. In embodiments where the external temperature is known, system 100 determines the initiation time of the area temperature action for the first area based on the external temperature. In some embodiments, system 100 generates (518) a predicted duration for reaching the comfortable temperature of the first area. System 100 may determine the initiation time of the area temperature action based on the predicted duration.

[0039] In response to determining the start time (516), system 100 sends a zone command (522) to the facility's temperature control system. The zone command includes the start time of the zone temperature action for the first zone.

[0040] refer to Figure 6 , Figure 6A sequence diagram of another example operation 600 for determining the recovery time for comfort conditions is shown. System 100 receives (602) data associated with multiple areas of the facility. For example, the system may receive (604) the area temperature of the areas, where each area temperature corresponds to a specific area. In some implementations, system 100 receives (606) the external temperature of the external areas of the facility and the area temperature of the areas within the facility.

[0041] In response to receiving (602) data from multiple regions, system 100 determines (608, 616) the initiation time of the region temperature action for each region. For example, system 100 may determine the initiation time of the first region based on the first region temperature of the first region, the second region temperature of the second region, and heat transfer characteristics. In embodiments where the external temperature is known, system 100 determines the initiation time of the region temperature action for the first region based on the external temperature. In some embodiments, system 100 generates (618) a predicted duration for reaching the comfortable temperature of the first region. System 100 may determine the initiation time of the region temperature action based on the predicted duration.

[0042] In some implementations, system 100 uses an AI or machine learning model to determine (608, 616) the initiation time of the zone temperature action for each zone. Therefore, system 100 uses a first set of data to train (608) optimizer 406 to take into account one or more heat transfer characteristics, and operates (616) the trained optimizer 406 to determine the initiation time of the zone temperature action.

[0043] System 100 trains (608) optimizer 406 to consider one or more heat transfer characteristics between adjacent pairs of areas of the facility. In some implementations, optimizer 406 considers heat transfer characteristics by explicitly estimating physical properties such as heat transfer coefficients or resistance values. In some implementations, optimizer 406 considers heat transfer characteristics by parameterizing a set of mathematical operations such that these operations behave like a physical thermal system, without estimating identifiable physical properties.

[0044] For example, a heat transfer characteristic can be associated with a first region and a second region, wherein the second region is located adjacent to the first region. In some implementations, system 100 can determine (610) the heat transfer characteristics between the first region and all regions adjacent to the first region. In some implementations, system 100 can identify (612) the heat transfer characteristics of insulators and non-insulators common to both the first and second regions. In some implementations, system 100 can train (608) optimizer 406 to determine the heat transfer characteristics by analyzing a first set of known start / stop conditions, the temperature of the first region, the temperature of the second region, and the start / stop conditions.

[0045] After training (608) the optimizer 406, system 100 operates the trained optimizer (616) to determine the initiation time of a zone temperature action. In some embodiments, system 100 may determine the initiation time of a zone temperature action by operating optimizer 406 to indicate an initiation signal or a non-initiation signal. The initiation signal and / or non-initiation signal may be based at least in part on a second set of a first zone temperature, a second zone temperature, and heat transfer characteristics. In some embodiments, system 100 generates (618) a predicted duration for reaching a comfortable temperature in the first zone. System 100 may determine the initiation time of a zone temperature action based on the predicted duration.

[0046] In response to determining the start time (616), system 100 sends a zone command (622) to the facility's temperature control system. The zone command includes the start time of the zone temperature action for the first zone.

[0047] As mentioned above, the optimizer 406 of system 100 can use an artificial intelligence or machine learning (“AI / ML”) model to identify heat transfer characteristics and / or determine the start-up time of zone temperatures. The input provided to the model is used to discover the infrastructure of the building's zones and the associated heat transfer conditions / thermal characteristics between zones. The AI / ML model can be trained to apply the facilities to optimize the optimal start-up sequence for the zones. After training, the AI / ML application can monitor the input / feedback on how it operates to apply the learned optimal room start-up sequence and for specific usage scenarios (such as different seasons, different weather conditions on a given day). For some implementations, system 100 can address scalability issues, as the number of zones can be increased to hundreds or even thousands.

[0048] For example, AI / ML applications can consume time-series data. Some data may be more relevant than others, such as the temperature of each area, the HVAC status (on / off or numerical) of each area, and / or external weather conditions (temperature, wind, and / or sunlight). AI / ML applications can learn to model the behavior of temperature regulation processes using these values, and the learned parameters can represent the thermal properties of facilities and associated equipment. The knowledge of AI / ML applications will be based on temperature changes between observed samples and will correlate these changes with the current temperature, the current HVAC system status, and the current external conditions. Temperature changes in each area can be strongly or weakly coupled to the temperature of that specific area, the temperature of neighboring areas, and / or the HVAC output in that area or neighboring areas.

[0049] The learning process can operate continuously using current values ​​and calculated changes. For example, AI / ML applications can accumulate time-series data and process it in large increments and / or process the entire warm-up cycle at once, rather than responding in small increments. In some implementations, system 100 can collect data or more data during periods of significant temperature variation rather than during stable operation. In some implementations, non-naturally occurring changes can be inserted into the process. For example, if start-up times are artificially separated from each other by a defined amount, system 100 can distinguish the impact of various HVAC outputs on each zone.

[0050] refer to Figure 7 , Figure 7 A separate connection process diagram is shown for yet another example operation 700 used to determine the recovery time for comfort conditions. For this example operation 700, the complete optimized start / stop process can be understood, designed, or implemented as separable subprocesses. Individual subprocesses can run on different processors and perform their functions on different time scales.

[0051] In one implementation, the room temperature control function reads current room temperature data and acts as a driver to move the room temperature toward the current setpoint over a period of several minutes, and applies preheating duration data to drive the room temperature toward the next predetermined setpoint. The startup optimizer receives room temperature data from one or more rooms, a setpoint schedule, and data representing heat transfer characteristics. The startup time optimizer calculates the startup duration for one or more rooms, performing this function on a timescale of, for example, hours or days. The learning process receives room temperature data from one or more rooms, possibly receiving numerical values ​​in real time and accumulating them into a time-series data structure, or possibly receiving a pre-compiled time-series data structure. The learning process calculates data that takes into account heat transfer characteristics within and between rooms, and delivers this data in a form that the optimizer can apply to calculate new startup duration data. The learning process performs its function over a period of, for example, many days.

[0052] The learning process and optimizer are designed to become more efficient as they run. Improvements are measured and guided by a performance evaluator algorithm (also known as a cost function). The cost function quantifies all aspects of "suboptimal performance." These aspects include warming up too late and warming up too early. The first leads to discomfort and occupant dissatisfaction. The second consumes more energy than necessary. These aspects are weighted and combined into a single performance metric. The optimizer and learning process are designed to adapt their operation in a way that minimizes the cost function over time.

[0053] Similar to example operation 600, example operation 700 of system 100 receives (702) data associated with multiple areas of the facility. The system may receive (704) the area temperature of the area, where each area temperature corresponds to a specific area. In some implementations, system 100 receives (706) the external temperature of the external areas of the facility and the area temperature of the areas within the facility.

[0054] Compared to example operation 600, example operation 700 separates training and operation into separable sub-processes. In response to receiving data from multiple regions (702), system 100 determines (708, 716) the initiation time of the region temperature action for each region. Specifically, system 100 uses an AI or machine learning model to determine the initiation time of the region temperature action for each region (708, 716). System 100 uses a first set of data to train (708) optimizer 406 to consider one or more heat transfer characteristics, and operates (716) optimizer 406 to determine the initiation time of the region temperature action.

[0055] In response to receiving (702) data from multiple regions, system 100 trains (708) optimizer 406 to consider one or more heat transfer characteristics between adjacent region pairs of the facility. In some implementations, system 100 may determine (710) the heat transfer characteristics between the first region and all regions adjacent to the first region. In some implementations, system 100 may identify (712) the heat transfer characteristics of insulators and non-insulators shared with the first and second regions.

[0056] In response to receiving (702) data from multiple regions, in addition to training (708) the optimizer 406, system 100 also operates (716) the optimizer to determine the start time for a region's temperature action. Typically, the optimizer is trained (708) before determining the start time; however, for instances where training is not yet complete, the optimizer may determine the start time based on defined settings until training is complete. In some implementations, system 100 generates (718) a predicted duration for reaching a comfortable temperature in a first region.

[0057] In response to determining the start time (716), system 100 sends a zone command (722) to the facility's temperature control system. The zone command includes the start time of the zone temperature action for the first zone.

[0058] Those skilled in the art will recognize that, for simplicity and clarity, this document does not depict or describe the complete structure and operation of all data processing systems suitable for use with this disclosure. Furthermore, the various features or processes described herein should not be considered essential to any or all implementations except those described herein. Various features may be omitted or repeated in various implementations. The various processes described may be omitted, repeated, performed sequentially, performed simultaneously, or performed in a different order. The various features and processes described herein may be combined in other implementations that may be described in the claims.

[0059] It is important to note that although this disclosure is described in the context of a fully functional system, those skilled in the art will understand that at least a portion of the mechanisms of this disclosure can be distributed in the form of instructions contained in various forms of machine-usable, computer-usable, or computer-readable media, and this disclosure applies equally regardless of the specific type of the instruction or signal-bearing medium or storage medium actually used to perform the distribution. Examples of machine-usable / readable or computer-usable / readable media include non-volatile, hard-coded media such as read-only memory (ROM) or erasable, electrically programmable read-only memory (EEPROM), and user-recordable media such as floppy disks, hard disk drives, and optical disc read-only memory (CD-ROM) or digital universal disc (DVD).

[0060] Although exemplary embodiments of the present disclosure have been described in detail, those skilled in the art will understand that various changes, substitutions, variations and modifications disclosed herein may be made without departing from the spirit and scope of the broadest form of the disclosure.

Claims

1. A building management system for determining recovery time for comfort conditions, comprising: An input component configured to receive temperatures of multiple zones of a facility, the multiple zones including a first zone and a second zone located adjacent to the first zone; A processor coupled to the input component, the processor identifies the heat transfer characteristics between the first region and the second region, and determines the activation time of the region temperature action of the first region based on the first region temperature, the second region temperature of the second region and the heat transfer characteristics; as well as An output component, coupled to the processor, sends a zone command to the temperature control system of the facility, the zone command including the start time of the zone temperature action of the first zone.

2. The building management system according to claim 1, wherein: The input component receives the external temperature of the external area of ​​the facility, and The processor determines the start time of the region temperature action of the first region by: determining the start time based on the external temperature.

3. The building management system according to claim 1, wherein, The processor determines the heat transfer characteristics between the first region and all regions adjacent to the first region.

4. The building management system according to claim 1, wherein, The processor identifies the thermal transfer characteristics of insulators and non-insulators shared by the first region and the second region.

5. The building management system according to claim 1, wherein, The processor generates a predicted duration for reaching a comfortable temperature in the first region and determines the start time of the temperature action in the region based on the predicted duration. A method for determining recovery time for comfort conditions, the method comprising: The temperature of multiple regions of the receiving facility is measured in multiple regions, including a first region and a second region located adjacent to the first region. Identify the heat transfer characteristics between the first region and the second region; The activation time of the zone temperature action in the first zone is determined based on the first zone temperature, the second zone temperature in the second zone, and the heat transfer characteristics; and A zone command is sent to the temperature control system of the facility, the zone command including the start time of the zone temperature action of the first zone.

6. The method according to claim 6, further comprising: Receiving the external temperature of the external area of ​​the facility, wherein determining the start time of the area temperature action of the first area includes: determining the start time based on the external temperature.

7. The method according to claim 6, wherein, Identifying the heat transfer characteristics between the first region and the second region includes: determining the heat transfer characteristics between the first region and all regions adjacent to the first region.

8. The method according to claim 6, wherein, Identifying the heat transfer characteristics between the first region and the second region includes: identifying the heat transfer characteristics of insulators and non-insulators common to both the first region and the second region.

9. The method according to claim 6, wherein, Determining the initiation time of the zone temperature action includes: generating a predicted duration for achieving a comfortable temperature in the first zone and determining the initiation time of the zone temperature action based on the predicted duration.

10. A building management system for determining recovery time for comfort conditions, comprising: An input component configured to receive temperatures of multiple zones of a facility, the multiple zones including a first zone and a second zone adjacent to the first zone; A processor coupled to the input component, the processor determining the activation time of the region temperature action of the first region based on the first region temperature of the first region, the second region temperature of the second region, and the heat transfer characteristics between the first region and the second region; as well as An output component, coupled to the processor, sends a zone command to the temperature control system of the facility, the zone command including the start time of the zone temperature action of the first zone.

11. The building management system according to claim 11, wherein: The input component receives the external temperature of the external area of ​​the facility, and The processor determines the start time of the region temperature action of the first region by: determining the start time based on the external temperature.

12. The building management system according to claim 11, wherein: The processor determines the heat transfer characteristics between the first region and all regions adjacent to the first region; and The processor determines the heat transfer characteristics of insulators and non-insulators shared by the first region and the second region.

13. The building management system of claim 11, further comprising an optimizer trained to determine the heat transfer characteristics by analyzing the temperature of the first zone, the temperature of the second zone, and a first set of known start / stop conditions, wherein, The trained optimizer is operated to indicate at least one of a start signal or a non-start signal, based at least in part on a second set of the first region temperature, the second region temperature, and the heat transfer characteristics.

14. The building management system according to claim 11, wherein, The processor generates a predicted duration for reaching a comfortable temperature in the first region and determines the start time of the temperature action in the region based on the predicted duration. A method for determining recovery time for comfort conditions, the method comprising: The temperature of multiple regions of the receiving facility is measured in multiple regions, including a first region and a second region located adjacent to the first region. The activation time of the zone temperature action in the first zone is determined based on the temperature of the first zone in the first region, the temperature of the second zone in the second region, and the heat transfer characteristics between the first and second regions; and A zone command is sent to the temperature control system of the facility, the zone command including the start time of the zone temperature action of the first zone.

15. The method of claim 16, further comprising: Receiving the external temperature of the external area of ​​the facility, wherein determining the start time of the area temperature action of the first area includes: determining the start time based on the external temperature.

16. The method according to claim 16, wherein, Determining the start time of the temperature action in the region includes: Determine the heat transfer characteristics between the first region and all regions adjacent to the first region; and Determine the heat transfer characteristics of the insulators and non-insulators shared by the first region and the second region.

17. The method according to claim 16, wherein, Determining the start time of the temperature action in the region includes: The optimizer is trained to determine the heat transfer characteristics by analyzing a first set of known start / stop conditions, including the temperatures of the first and second regions. The trained optimizer is operated to indicate at least one of the start-up signal or non-start-up signal based at least in part on a second set of the first region temperature, the second region temperature, and the heat transfer characteristics.

18. The method according to claim 16, wherein, Determining the initiation time of the zone temperature action includes: generating a predicted duration for achieving a comfortable temperature in the first zone and determining the initiation time of the zone temperature action based on the predicted duration.

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