Methods and systems for scheduling HVAC systems

Abstract

In some aspects, a method for scheduling a heating, ventilation, and air conditioning (HVAC) system is provided. The HVAC system includes an air conditioning plant, at least one air handling unit (AHU) connected to the air conditioning plant, and the at least one AHU is used to serve a plurality of zones. The method includes obtaining zone environmental information including zone temperatures, zone air quality indicators, and zone setpoints for the plurality of zones including zone temperature setpoints and zone air quality setpoints. The method further includes obtaining a conditioned air temperature and a conditioned air quality indicator, and a fresh air temperature for mixing with return air of the conditioned air to form pre-conditioned air, and determining a minimum conditioned air supply rate and a return air ratio based on a conditioned air function of parameters including the obtained information.

Description

Technical Field

[0001] The various aspects involve methods and systems for scheduling heating, ventilation and air-conditioning (HVAC) systems. Background Technology

[0002] Buildings consume significant amounts of energy, with heating, ventilation, and air conditioning (HVAC) systems accounting for a large proportion of this energy consumption. Commercial HVAC systems are variable air volume (VAV) or variable refrigerant volume (VRV) systems that supply cooling energy to multiple zones. The controllers for such systems can be simple thermostats or optimization-based controllers (e.g., model predictive control, MPC). Because MPC can handle complex constraints, nonlinear dynamics, and physical behavior, many HVAC control methods employ a centralized architecture, aiming to minimize energy consumption across all zones through MPC. However, centralized control architectures are unsuitable due to computational complexity and implementation challenges associated with large-scale deployments.

[0003] There is a need to provide improved methods and systems for scheduling HVAC systems. Summary of the Invention

[0004] According to a first aspect of this disclosure, a method for scheduling a heating, ventilation, and air conditioning (HVAC) system is provided. The HVAC system may include air conditioning equipment, at least one air handling unit (AHU) connected to the air conditioning equipment, and the at least one AHU serving multiple zones. The method may include: obtaining zone environmental information, including zone temperature, zone air quality index, and zone setpoints for multiple zones, the zone setpoints including zone temperature setpoints and zone air quality setpoints; obtaining a conditioned air temperature and a conditioned air quality index associated with the at least one AHU, and a fresh air temperature for mixing with return air of the conditioned air to form pre-conditioned air; and determining, for the at least one AHU and a prediction range, a minimum conditioned air supply rate and a return air ratio based on a conditioned air function including parameters including zone temperature, conditioned air temperature, fresh air temperature, zone air quality index, and conditioned air quality index, to jointly satisfy the zone setpoints of the multiple zones.

[0005] According to a second aspect of this disclosure, a system for scheduling a heating, ventilation, and air conditioning (HVAC) system is provided. The HVAC system may include air conditioning equipment and at least one air handling unit (AHU) connected to the air conditioning equipment, the at least one AHU serving multiple zones. The system may include: a zone module for obtaining zone environmental information, including zone temperature, zone air quality index, and zone setpoints for multiple zones, the zone setpoints including zone temperature setpoints and zone air quality setpoints; an input module for obtaining conditioned air temperature and conditioned air quality index associated with at least one AHU, and fresh air temperature for mixing with return air of the conditioned air to form pre-conditioned air; and a scheduler for determining a minimum conditioned air supply rate and return air ratio for at least one AHU and a forecast range, based on a conditioned air function including parameters including zone temperature, conditioned air temperature, fresh air temperature, zone air quality index, and conditioned air quality index, to jointly satisfy the zone setpoints of multiple zones. Attached Figure Description

[0006] Throughout the accompanying drawings, it should be noted that the same reference numerals are used to depict the same or similar elements, features, and structures. The drawings are not necessarily drawn to scale, and the emphasis is generally on illustrating various aspects of this disclosure. In the following description, some aspects of this disclosure are described with reference to the following drawings, in which:

[0007] Figure 1 This is a flowchart illustrating example methods for scheduling a heating, ventilation, and air conditioning (HVAC) system according to various embodiments of the present disclosure;

[0008] Figure 2 This is a block diagram illustrating an example system for scheduling an HVAC system according to various embodiments of the present disclosure;

[0009] Figure 3 This is a block diagram illustrating an example HVAC system according to various embodiments of the present disclosure;

[0010] Figure 4 This is a block diagram illustrating example methods for scheduling an HVAC system according to various embodiments of the present disclosure;

[0011] Figure 5 This is a block diagram illustrating a multilayer neural network used in an example method for scheduling an HVAC system according to various embodiments of the present disclosure;

[0012] Figure 6 The upper figure depicts a comparison of actual temperature with estimated temperature using an example model for scheduling an HVAC system according to various embodiments of the present disclosure, and the lower figure depicts the error between actual temperature and estimated temperature.

[0013] Figure 7 This is a graph showing a comparison between measured values ​​of CO2 concentration according to various embodiments of the present disclosure and estimated values ​​using an example model for scheduling HVAC systems;

[0014] Figure 8 These are diagrams illustrating experimental results according to various embodiments of the present disclosure; and

[0015] Figure 9 This is a block diagram illustrating an example electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0016] The following detailed description refers to the accompanying drawings, which illustrate specific details and aspects in which this disclosure can be implemented. One or more aspects have been described in sufficient detail to enable those skilled in the art to implement this disclosure. Other aspects may be used, and structural, logical, and / or electrical modifications may be made, without departing from the scope of this disclosure. The aspects of this disclosure are not necessarily mutually exclusive, as some aspects may be combined with one or more other aspects to form new aspects or embodiments. The descriptions of the aspects relate to methods, and the descriptions of the aspects relate to devices. However, it is understood that aspects related to methods may also be applicable to devices, and vice versa.

[0017] It should be understood that, unless the context clearly indicates otherwise, the singular terms “a,” “an,” and “the” include the plural. Similarly, “or” includes “and” unless the context clearly indicates otherwise.

[0018] It should be further understood that the terms “comprise” (and any form of inclusion, such as “comprises” and “comprising”), “have” (and any form of having, such as “has” and “having”), “include” (and any form of inclusion, such as “includes” and “including”), and “contain” (and any form of inclusion, such as “contains” and “containing”) are open-ended connecting verbs. Therefore, a method or apparatus that “comprises,” “has,” “includes,” or “includes” one or more steps or elements possesses, but is not limited to, possessing only those steps or elements. Similarly, a method step or apparatus element that “comprises,” “has,” “includes,” or “includes” one or more features possesses, but is not limited to, possessing only those features. Furthermore, an apparatus or structure configured in a certain way is configured at least in this manner, but may also be configured in a manner not listed.

[0019] The approximate language used throughout this specification and claims can be used to modify any quantitative representations that allow for variation without altering the underlying functionality associated with them. Therefore, values ​​modified by one or more terms (such as "approximately," "substantially") are not limited to specified exact values, but rather fall within tolerances acceptable for application in embodiments of the invention. In some cases, approximate language may correspond to the instrumental precision of the measured values.

[0020] As used herein, phrases of the form "at least one of A or B" may include A, or B, or A and B. Accordingly, phrases of the form "at least one of A, B, or C," or phrases that include further listed items, may include any and all combinations of one or more of the related listed items.

[0021] The term “exemplary” as used herein can mean “as an example, instance, or illustration.” Any aspect or design described herein as “exemplary” is not necessarily to be construed as superior to or better than other aspects or designs.

[0022] The terms “at least one” and “one or more” can be understood to include a number greater than or equal to one (e.g., one, two, three, four, etc.). The term “multiple” can be understood to include a number greater than or equal to two (e.g., two, three, four, five, etc.). The phrase “at least one of” relating to a group of elements can be used to refer to at least one element in a group consisting of these elements. For example, the phrase “at least one of” relating to a group of elements in this document can be used to refer to one of the following: listing one element, multiple listing one element, multiple listing a single element, or multiple listing elements.

[0023] The terms "plural" and "multiple" in the description and claims explicitly refer to a quantity greater than one. Therefore, any phrase explicitly referencing these terms (e.g., "a plurality of" or "multiple") explicitly refers to more than one of the objects when referring to a quantity of objects. The terms "group," "set," "collection," "series," "sequence," "grouping," etc., in the description and claims, if applicable, refer to a quantity equal to or greater than one, i.e., one or more.

[0024] As used herein, the term "data" can be understood to include information in any suitable analog or digital form, such as information provided as a file, a portion of a file, a set of files, a signal or stream, a portion of a signal or stream, a set of signals or streams, etc. Furthermore, the term "data" can also be used to refer to information, such as in the form of a pointer. However, the term "data" is not limited to the examples above, can take various forms, and represents any information as understood in the art. As described herein, any type of information can be processed by one or more processors in an appropriate manner, such as as data.

[0025] The terms "processor," "scheduler," or "controller" as used herein can be understood as any entity capable of processing data. Data can be processed according to one or more specific functions performed by the processor, scheduler, or controller. Furthermore, the processor, scheduler, or controller as used herein can be understood as any type of circuit, such as any type of analog or digital circuit. Therefore, a processor, scheduler, or controller can be, or may include, analog circuits, digital circuits, mixed-signal circuits, logic circuits, processors, microprocessors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), integrated circuits, application-specific integrated circuits (ASICs), etc., or any combination thereof. The corresponding functions will be further described in detail below, and can also be understood as any other type of implementation of a processor, scheduler, controller, or logic circuit. It should be understood that any two (or more) processors, schedulers, controllers, or logic circuits detailed herein can be implemented as a single entity with equivalent or similar functionality, and conversely, any single processor, scheduler, controller, or logic circuit detailed herein can be implemented as two (or more) independent entities with equivalent or similar functionality.

[0026] The term “memory” as used in this article can be understood to include any suitable type of memory or storage device, such as hard disk drive (HDD), solid-state drive (SSD), flash memory, etc.

[0027] The term "module" as detailed herein refers to an application-specific integrated circuit (ASIC), electronic circuitry, combinational logic circuitry, field-programmable gate array (FPGA), a (shared, dedicated, or grouped) processor that executes code, other suitable hardware components that provide the aforementioned functionality, or a combination of some or all of the above, such as in a system-on-a-chip. The term "module" may include (shared, dedicated, or grouped) memory storing code executed by the processor.

[0028] The differences between software and hardware implementations of data processing can be blurred. The processors, schedulers, controllers, and / or circuits detailed in this article can be implemented in software, hardware, and / or a hybrid approach including both software and hardware.

[0029] The term “system” (e.g., trading support system, computing system, etc.) as detailed herein can be understood as a set of interacting elements, which, by way of example and not limitation, can be one or more mechanical components, one or more electrical components, one or more instructions (e.g., encoded in a storage medium), and / or one or more processors, etc.

[0030] Unless otherwise stated, the terms “first,” “second,” and “third” used in detail herein are used to distinguish one element from another similar element and do not necessarily indicate order or relative importance. For example, “first transaction data” and “second transaction data” can be used to distinguish two transactions based on the exchange of two different foreign currencies.

[0031] Various embodiments of this disclosure provide a method and system for scheduling a heating, ventilation, and air conditioning (HVAC) system associated with a building, and more specifically, for optimizing multiple building performance parameters when providing an environment (e.g., a desired indoor environment) for a region of the building. Those skilled in the art will understand that the aforementioned region can refer to any one or more areas within a building, or a walled or enclosed area, such as, but not limited to, rooms (e.g., offices, meeting rooms, apartment rooms, hotel rooms, etc.), open-plan office spaces, lecture halls, theaters, etc. Those skilled in the art will understand that the aforementioned environment can refer to the indoor environment within an area regulated or controlled by an air conditioning system. Those skilled in the art will also understand that, in order to optimize multiple building performance parameters when providing an environment related to a region of the building, the method and system for scheduling the HVAC system can also be applied or employed for each region of the building (e.g., each predetermined or selected region). Therefore, building performance parameters for each region of the building can be optimized.

[0032] The aspects described herein seek to provide a method for scheduling a heating, ventilation, and air conditioning (HVAC) system. The HVAC system may include air conditioning equipment, at least one air handling unit (AHU) connected (fluidically) to the air conditioning equipment, and the at least one AHU serving multiple zones. The proposed method may include obtaining zone environmental information (e.g., zone-level information for each zone during the current time period), which includes zone temperature, zone air quality indicators (e.g., zone carbon dioxide (CO2) concentration data), and zone setpoints (e.g., zone temperature setpoints and zone air quality setpoints) for each of the multiple zones. The proposed method may also include obtaining AHU-level information during the current time period, including the conditioned air temperature and conditioned air quality indicators associated with at least one AHU, and the fresh air temperature for mixing with return air from the conditioned air to form pre-conditioned air. The proposed method may further include determining, for at least one AHU (at the AHU level) and the current time period of the forecast range, a minimum regulated air supply rate (e.g., at the regional level) and a return air ratio based on regulated air functions of parameters (e.g., obtained regional-level information for each region and obtained AHU-level information including regional temperature, regulated air temperature, fresh air temperature, regional air quality index, and regulated air quality index) to satisfy all regional setpoints for multiple regions.

[0033] According to some embodiments, regional air quality indicators may include regional CO2 concentration data. Regional CO2 concentration data for subsequent time periods within the prediction range may be determined as a multi-component function by a dynamic CO2 concentration model, the multi-component function comprising multiple components related to regional parameters selected from a group of air volume, air density, occupants, and / or equipment CO2 production rates for the respective region among multiple regions.

[0034] The proposed method may include three stages. The first stage of the proposed method may include minimizing the energy cost function of each AHU (e.g., determining the minimum regulated air supply rate and return air ratio of the AHU based on the obtained regional-level information and AHU-level information for each region). This may mean that in the first stage, the proposed method includes determining the minimum regulated air supply rate and return air ratio of each AHU connected to the air conditioning unit.

[0035] The second stage of the proposed method may include minimizing the energy consumption of the air conditioning equipment (e.g., the product of the air conditioning equipment efficiency and the air conditioning load) at the air conditioning equipment level based on the efficiency of the air conditioning equipment (e.g., varying with the air conditioning load) and the sum of the air conditioning loads of all AHUs (e.g., the air conditioning load of each AHU includes the sum of the air conditioning loads of each area served by the AHU). This may mean that in the second stage, the proposed method includes determining the optimal air conditioning load and thus determining the damper opening (e.g., the AHU-level damper) used to change the return air ratio by adjusting the position of the damper opening (e.g., the opening of the AHU-level damper). In the second stage, a determined minimum regulated air supply rate can be maintained (e.g., fixed) and air quality requirements (zone setpoints) can be met while optimizing the determined return air ratio. By optimizing the return air ratio, the air conditioning load can be optimized accordingly, which in turn affects the efficiency of the air conditioning equipment. Each AHU may have a damper for fresh air supply (e.g., an AHU-class damper), and the damper (e.g., an AHU-class damper) can ensure air quality in each zone by mixing fresh and return air in the AHU before it is pumped to each individual zone.

[0036] The third stage of the proposed method may include optimizing the energy consumption of each AHU (e.g., fan energy consumption). This may mean that in the third stage, the proposed method includes mapping the AHU fan supply pressure and zone damper opening to a regulated air supply rate. Each zone may have its own zone damper for regulated air supply. The zone damper can handle zone temperature. Therefore, the minimum regulated air supply rate determined in the first stage can be optimized in the third stage; that is, the optimized air supply rate may not be minimum. This may mean that the air supply rate optimized in the third stage may be greater than the minimum regulated air supply rate determined in the first stage, for example, due to the adjusted return air ratio in the second stage.

[0037] In some cases, aspects of the systems and techniques described herein offer technological improvements and advantages over existing methods. For example, the proposed systems and methods can provide a technical solution for scheduling HVAC systems to meet zone thermal comfort and air quality requirements (e.g., setpoints) while ensuring low energy consumption. The proposed methods are computationally feasible and scalable for real-time operation. For example, the proposed methods can be implemented to schedule HVAC systems with multiple AHUs, each serving multiple zones, meeting the thermal comfort and air quality requirements (e.g., setpoints) of all zones. Alternatively, the proposed methods can be implemented to schedule HVAC systems with multiple AHUs, each serving multiple zones, meeting the thermal comfort requirements of all zones but satisfying the air quality requirements of selected zones (e.g., served by one AHU or selected AHUs).

[0038] The following embodiments relate to various aspects of this disclosure.

[0039] Example 1 is a method for scheduling a heating, ventilation, and air conditioning (HVAC) system, wherein the HVAC system includes air conditioning equipment, at least one air handling unit (AHU) connected to the air conditioning equipment, and the at least one AHU serves multiple zones. The method includes: obtaining zone environmental information, including zone temperature, zone air quality index, and zone setpoints for multiple zones, the zone setpoints including zone temperature setpoints and zone air quality setpoints; obtaining a conditioned air temperature and a conditioned air quality index associated with the at least one AHU, and a fresh air temperature for mixing with return air of the conditioned air to form pre-conditioned air; and determining, for the at least one AHU and a prediction range, a minimum conditioned air supply rate and a return air ratio based on a conditioned air function including parameters including zone temperature, conditioned air temperature, fresh air temperature, zone air quality index, and conditioned air quality index, to jointly satisfy the zone setpoints for the multiple zones.

[0040] In Example 2, the subject of Example 1 may optionally include that the adjusted air quality index is determined by the air quality index of the return air, the air quality index of the fresh air, and the return air ratio.

[0041] In Example 3, the subject matter of Example 1 or Example 2 may optionally include regional air quality indicators including regional carbon dioxide (CO2) concentration data, wherein the regional CO2 concentration data for subsequent time periods within the prediction range is determined by a dynamic model of CO2 concentration as a multi-component function, the multi-component function including multiple components related to regional parameters selected from a group of air volume, air density, occupant and / or equipment CO2 production rates of the respective regions in multiple regions.

[0042] In Example 4, the subject matter of any of Examples 1 to 3 may optionally include that the regional temperature for a subsequent time period is defined as the regional temperature for the current time period within the forecast range, the regional air conditioning load, the mass flow rate of the regulated air supply for the corresponding region in multiple regions, and a temperature linear function of the regulated air temperature.

[0043] In Example 5, the subject matter of any of Examples 1 to 4 may optionally include at least one AHU including a damper opening for changing the return air ratio by adjusting the position of the damper opening.

[0044] In Example 6, the subject matter of Example 5 may optionally include determining the average return air ratio between the positions of the damper opening and determining the difference between the return air ratio and the average return air ratio when the damper opening is at each of the positions.

[0045] In Example 7, the subject of Example 6 may optionally include setting a lower limit and an upper limit for the air conditioning load associated with at least one AHU, wherein the lower limit is set when the return air ratio is at its maximum and the upper limit is set when the return air ratio is zero, wherein the air conditioning load is set between the lower limit and the upper limit.

[0046] In Example 8, the subject matter of Example 7 may optionally include: (i) obtaining parameters related to the performance coefficient of the air conditioning unit; (ii) determining the parameters related to the performance coefficient of the air conditioning unit as a first parameter if the air conditioning load associated with at least one AHU is less than or equal to a first predetermined threshold; (iii) determining the parameters related to the performance coefficient of the air conditioning unit as a second parameter if the air conditioning load associated with at least one AHU is less than or equal to a second predetermined threshold and greater than or equal to the first predetermined threshold; and (iv) continuing the steps in (iii) until the air conditioning load associated with at least one AHU is greater than a final predetermined threshold, and determining the parameters related to the performance coefficient of the air conditioning unit as the final parameter.

[0047] In Example 9, the subject of Example 8 may optionally include an optimization function that optimizes the return air ratio based on parameters related to the performance coefficient of the determined air conditioning equipment and the difference between the return air ratio and the average return air ratio at each position of the damper opening.

[0048] In Example 10, the subject of Example 9 may optionally include, based on the area damper opening of multiple areas and the fan supply pressure of at least one AHU, the mass flow rate of regulated air coupled and mapped to the regulated air supply of multiple areas.

[0049] In Example 11, the subject of Example 10 may optionally include: transmitting the optimized return air ratio to a scheduler; the scheduler receiving the optimized return air ratio and energy efficiency data of the air conditioning equipment; balancing parameters related to the optimized return air ratio and the coefficient of performance of the air conditioning equipment over a subsequent time period; calculating an air supply strategy based on the balance, the air supply strategy including regulated air supply allocation for multiple areas over a subsequent time period to minimize the energy consumption of the air conditioning equipment while meeting area setpoints; and transmitting the air supply strategy to multiple areas.

[0050] Example 12 is a system for scheduling a heating, ventilation, and air conditioning (HVAC) system. The HVAC system may include air conditioning equipment and at least one air handling unit (AHU) connected to the air conditioning equipment, the at least one AHU serving multiple zones. The system may include: a zone module for obtaining zone environmental information, including zone temperature, zone air quality index, and zone setpoints for multiple zones, the zone setpoints including zone temperature setpoints and zone air quality setpoints; an input module for obtaining the conditioned air temperature and conditioned air quality index associated with at least one AHU, and the fresh air temperature for mixing with return air of the conditioned air to form pre-conditioned air; and a scheduler for determining a minimum conditioned air supply rate and return air ratio for at least one AHU and a forecast range, based on a conditioned air function including parameters including zone temperature, conditioned air temperature, fresh air temperature, zone air quality index, and conditioned air quality index, to jointly satisfy the zone setpoints of multiple zones.

[0051] In Example 13, the subject matter of Example 12 may optionally include that the adjusted air quality index is determined by the air quality index of the return air, the air quality index of the fresh air, and the return air ratio.

[0052] In Example 14, the subject matter of Example 12 or Example 13 may optionally include regional air quality indicators including regional carbon dioxide (CO2) concentration data, wherein the regional carbon dioxide (CO2) concentration data for subsequent time periods within the prediction range is determined by a dynamic model of carbon dioxide (CO2) concentration as a multi-component function, the multi-component function including multiple components related to regional parameters selected from a group of air volume, air density, occupant and / or equipment carbon dioxide (CO2) production rates of the respective regions in multiple regions.

[0053] In Example 15, the subject matter of any of Examples 12 to 14 may optionally include that the regional temperature for a subsequent time period is defined as the regional temperature for the current time period within the forecast range, the regional air conditioning load, the mass flow rate of the regulated air supply for the corresponding region in the plurality of regions, and a temperature linear function of the regulated air temperature.

[0054] In Example 16, the subject matter of any of Examples 12 to 15 may optionally include at least one AHU including a damper opening for changing the return air ratio by adjusting the position of the damper opening.

[0055] In Example 17, the subject of Example 16 may optionally include that the scheduler is also used to: determine the average return air ratio between the positions of the damper opening, and determine the difference between the return air ratio and the average return air ratio when the damper opening is at each of the positions.

[0056] In Example 18, the subject of Example 17 may optionally include that the scheduler is also configured to: set a lower limit and an upper limit for the air conditioning load associated with at least one AHU, wherein the lower limit is set when the return air ratio is at its maximum and the upper limit is set when the return air ratio is zero, wherein the air conditioning load is set between the lower limit and the upper limit.

[0057] In Example 19, the subject matter of Example 18 may optionally include that the input module is further configured to: (i) obtain parameters related to the performance coefficient of the air conditioning equipment; wherein the scheduler is further configured to: (ii) determine the parameters related to the performance coefficient of the air conditioning equipment as a first parameter if the air conditioning load associated with at least one AHU is less than or equal to a first predetermined threshold; (iii) determine the parameters related to the performance coefficient of the air conditioning equipment as a second parameter if the air conditioning load associated with at least one AHU is less than or equal to a second predetermined threshold and greater than or equal to the first predetermined threshold; and (iv) continue the steps as in (iii) until the air conditioning load associated with at least one AHU is greater than a final predetermined threshold, and determine the parameters related to the performance coefficient of the air conditioning equipment as the final parameter.

[0058] In Example 20, the subject of Example 19 may optionally include that the scheduler is also used to: optimize the return air ratio based on parameters related to the performance coefficient of the determined air conditioning equipment and an optimization function of the difference between the return air ratio and the average return air ratio when the damper opening is at each position.

[0059] Figure 1 This is a flowchart illustrating an example method 100 for scheduling a heating, ventilation, and air conditioning (HVAC) system according to various embodiments of the present disclosure. The HVAC system may include air conditioning equipment, at least one air handling unit (AHU) connected to the air conditioning equipment, and the at least one AHU serving multiple areas.

[0060] In the context of the various embodiments, the term "heating, ventilation, and air conditioning (HVAC)" refers to the use of various technologies to control the temperature, humidity, and / or purity of air in an enclosed space to provide thermal comfort and desired indoor air quality. It should be understood that the proposed methods are intended for use with any air conditioning system, including heating, cooling, refrigeration, ventilation, and any other combination of air conditioning systems.

[0061] According to various non-limiting embodiments, method 100 may include the following steps.

[0062] In step 101, regional environmental information can be obtained, including regional temperature, regional air quality index, and regional setpoints for each of multiple regions. The regional setpoints for each of the multiple regions may include regional temperature setpoints and regional air quality setpoints. The setpoints may include lower and upper limits. At least one AHU can serve multiple regions, and therefore, step 101 may include obtaining regional environmental information from each of the multiple regions simultaneously or continuously. Each of the multiple regions may have a temperature sensor and a regional air quality index sensor that provide regional environmental information for the corresponding region. The regional setpoints can be set by users in the respective regions of the multiple regions or centrally set (defined as the desired range of regional temperature and regional air quality index). The regional setpoints can be set differently for different regions within the multiple regions.

[0063] In step 103, the conditioned air temperature and conditioned air quality index of the conditioned air associated with at least one AHU can be obtained, as well as the fresh air temperature for mixing with the return air of the conditioned air to form the pre-conditioned air. The conditioned air can be supplied to at least one AHU by an air conditioning unit connected to at least one AHU. Temperature sensors and zone air quality index sensors can also be arranged to measure the conditioned air temperature and conditioned air quality index of the conditioned air supplied to at least one AHU. Similarly, temperature sensors and zone air quality index sensors can also be arranged to measure the fresh air temperature of the fresh air. Return air refers to air returning to the HVAC system from multiple zones served by at least one AHU. That is, return air refers to a combination of return air from corresponding zones in multiple zones served by at least one AHU.

[0064] It should be understood that although this article describes temperature sensors and regional air quality index sensors, these two sensors can be combined into one sensor, and other sensors can be used, as long as the sensors provide the required information.

[0065] In step 105, for at least one AHU and the prediction range, a minimum conditioned air supply rate and return air ratio can be determined based on a conditioned air function including parameters such as zone temperature, conditioned air temperature, fresh air temperature, zone air quality index, and conditioned air quality index, to collectively meet zone settings for multiple zones. In some embodiments, the minimum conditioned air supply rate and return air ratio can be determined to meet zone settings for each of the multiple zones served by at least one AHU. In some embodiments, the minimum conditioned air supply rate and return air ratio can be determined to meet zone settings for a selected zone among the multiple zones served by at least one AHU. The return air ratio refers to the ratio of the amount of return air (e.g., volume) to the amount of conditioned air (e.g., volume).

[0066] Figure 2 This is a block diagram illustrating an example system 200 for scheduling an HVAC system according to various embodiments of the present disclosure. The HVAC system may include air conditioning equipment and at least one air handling unit (AHU) connected to the air conditioning equipment, the at least one AHU serving multiple areas.

[0067] According to various non-limiting embodiments, system 200 may include a region module 210, an input module 220, and a scheduler 230. System 200 may also include... Figure 2 Other modules not shown. System 200 can be integrated into an HVAC system or attached as a standalone system to an HVAC system.

[0068] According to various non-limiting embodiments, the area module 210 can be used to obtain area environmental information, including area temperature, area air quality index, and area setpoints for each of multiple areas. The area setpoints for each of the multiple areas may include area temperature setpoints and area air quality setpoints. The area module 210 may include multiple sub-modules, each set in a corresponding area of ​​the multiple areas. The area module 210 may also include a main sub-module that communicates with each of the multiple sub-modules and processes the corresponding area environmental information from the multiple areas. The processed area environmental information can be provided to the scheduler 230.

[0069] According to various non-limiting embodiments, input module 220 can be used to obtain conditioned air temperature and conditioned air quality indicators of conditioned air associated with at least one AHU, and fresh air temperature for mixing with return air of conditioned air to form pre-conditioned air. Input module 220 can also be used to obtain the air quality indicators of fresh air. Input module 220 may include a first submodule and a second submodule, the first submodule being used to obtain the conditioned air temperature and conditioned air quality indicators of conditioned air associated with at least one AHU, and the second submodule being used to obtain the fresh air temperature and air quality indicators of fresh air. Input module 220 can provide the obtained information to scheduler 230.

[0070] According to various non-limiting embodiments, for at least one AHU and a prediction range, the scheduler 230 can be used to determine a minimum regulated air supply rate and return air ratio based on a regulated air function including parameters such as zone temperature, regulated air temperature, fresh air temperature, zone air quality index, and regulated air quality index, in order to jointly meet the zone settings of each of multiple zones. The prediction range may include multiple discrete time intervals. At least one AHU may include multiple AHUs, and accordingly, the scheduler 230 can determine a minimum regulated air supply rate and return air ratio for a given AHU among the multiple AHUs and the prediction range, based on a regulated air function including parameters such as zone temperature, regulated air temperature, fresh air temperature, zone air quality index, and regulated air quality index obtained from multiple zones served by the given AHU, in order to jointly meet the zone settings of the multiple zones served by the given AHU. Scheduler 230 can be used to output optimized data or information (e.g., adjusted air supply rate, return air ratio, air conditioning load) for each zone of a building and / or AHU and / or air conditioning equipment (e.g., within the forecast range) to provide optimized energy consumption and occupant thermal comfort, such as by solving the optimization or scheduling problems described herein.

[0071] According to various non-limiting embodiments, system 200 may also include controllers, such as, but not limited to, VAV controllers, cooler controllers, and fan controllers, which operate based on outputs from scheduler 230 and are typically managed by a building energy management system (BEMS).

[0072] Figure 3 This is a block diagram illustrating an example HVAC system 300 according to various embodiments of the present disclosure. The HVAC system 300 may include an air conditioning unit 320 and an air handling unit (AHU) 310 fluidly connected to the air conditioning unit 320, wherein the AHU 310 is used to serve multiple zones (e.g., zone 1, ..., zone i, ..., zone n), for example, as... Figure 3Zones 311, 312, and 313 are shown. The method 100 described above can be used to schedule the HVAC system 300. The HVAC system 300 can be scheduled (e.g., controlled) by system 200. Air conditioning unit 320 can provide (e.g., generate) conditioned air 301 for supply to AHU 310. AHU 310 can provide (e.g., circulate) conditioned air 301 to multiple zones (e.g., 311, 312, and 313). Return air 302 from multiple zones 311, 312, and 313 can be mixed with fresh air 303 to form pre-conditioned air 304, and the pre-conditioned air 304 from return air 302 and fresh air 303 can be circulated to air conditioning unit 320. Pre-conditioned air 304 can be conditioned by conditioning water 306 (e.g., chilled water, hot water) to produce conditioned air 301.

[0073] According to various non-limiting embodiments, the region module 210 of system 200 ( Figure 3 (Not shown) can be arranged in multiple zones 311, 312, and 313. Zone module 210 can be used to obtain zone environment information for multiple zones 311, 312, and 313. For example, zone 311 is a meeting room, zone 312 is an office shared by several staff members, and zone 313 is an office occupied by one staff member and office equipment.

[0074] In some embodiments, the region module 210 can be used to obtain regional environmental information of multiple regions 311, 312, and 313, that is, to obtain regional environmental information of all regions served by AHU 310. In some embodiments, the region module 210 can be used to obtain regional environmental information of regions 312 and 313, that is, to obtain regional environmental information of selected regions served by AHU 310. This may mean that, for AHU 310 serving multiple regions 311, 312, and 313 and its prediction range, the minimum regulated air supply rate and return air ratio can be determined based on the regional environmental information of the selected regions. This may also mean that, for AHU 310 serving multiple regions 311, 312, and 313 and its prediction range, the minimum regulated air supply rate and return air ratio can be determined based on the dynamic regional environmental information of the selected regions and the preset regional environmental information of the remaining regions among the multiple regions.

[0075] In some embodiments, the area module 210 can be used to obtain area environmental information, including the area temperature, area air quality index, area temperature setpoint, and area air quality setpoint for each of a plurality of areas 311, 312, and 313; that is, to obtain all area environmental information for all areas served by AHU 310. In some embodiments, the area module 210 can be used to obtain area environmental information including the area temperature and area temperature setpoint for each of a plurality of areas 311, 312, and 313, and to obtain area environmental information including the area air quality index and area air quality setpoint for areas 312 and 313; that is, to obtain a portion of the area environmental information for a selected area served by AHU 310. This could mean that, for AHU 310 serving multiple areas 311, 312, and 313 and the forecast range, a minimum regulated air supply rate and return air ratio can be determined based on the area environmental information of the selected area and a portion of the area environmental information of the remaining areas among the plurality of areas. This also means that for AHU 310 serving multiple regions 311, 312, and 313 and the forecast range, the minimum regulated air supply rate and return air ratio can be determined based on the regional environmental information of the selected region, a portion of the regional environmental information of the remaining regions, and other preset portions of the regional environmental information of the remaining regions in multiple regions.

[0076] According to various non-limiting embodiments, the input module 220 of system 200 ( Figure 3 The input module 220 (not shown) can be used to obtain the conditioned air temperature and conditioned air quality indicators associated with the AHU 310. The input module 220 can also be used to obtain the fresh air temperature for mixing with the return air of the conditioned air to form the pre-conditioned air. In some embodiments, the input module 220 may include a temperature sensor 314 for obtaining the conditioned air temperature. In some embodiments, the input module 220 may also include a pressure sensor 315 for obtaining the conditioned air pressure. In some embodiments, the input module 220 may also include an air quality sensor (not shown) for obtaining the air quality index of the conditioned air. In some embodiments, the input module 220 may also include a temperature sensor 316 and an air quality sensor 317 (e.g., a CO2 concentration sensor) for obtaining the return air temperature and air quality index.

[0077] According to various non-limiting embodiments, the scheduler 230 of system 200 ( Figure 3(Not shown) can be used for AHU310 and for the forecast range to determine the minimum regulated air supply rate and return air ratio based on the regulated air function of parameters in order to meet the zone setpoints of multiple zones 311, 312, and 313, including zone temperature, regulated air temperature, fresh air temperature, zone air quality index, and regulated air quality index.

[0078] According to various non-limiting embodiments, regional air quality indicators may include regional carbon dioxide (CO2) concentration data and particulate matter 2.5 (PM2.5). 2.5 Data, humidity data, airborne allergen data (e.g., dust mites), chemical hazard data, air quality index, etc.

[0079] The following describes a predictive model for providing conditioned air with constraints on heat and air quality.

[0080] According to various non-limiting embodiments, a model for providing the energy cost function J of regulated air is provided, as follows:

[0081] Limited by:

[0082]

[0083]

[0084] in,

[0085] ·P u,f (k) - The wind turbine power function of AHU u within the discrete time interval k.

[0086] ·P c (k) - Power function of air conditioning equipment within discrete time interval k.

[0087] • Δ - Sampling period, i.e., the length of the selected discrete time interval.

[0088] • z - The number of regions in the target building.

[0089] ·u- An AHU,

[0090] ·{u1,…,u z The various regions in AHU u

[0091] ·f r -Thermodynamic model of region r

[0092] ·T r (k) - Temperature of region r within discrete time interval k,

[0093] · -k inner region r regulated air supply flow rate

[0094] ·T c (k) - Temperature of the cold air supply within the discrete time interval k.

[0095] ·Q r (k) - Environmental air conditioning load of region r within discrete time interval k.

[0096] ·[T r,l (k),T r,u [(k)] - The heat setpoint for region r, i.e., the lower / upper limit of the temperature.

[0097] • The coupling of h-region flow rate, region damper opening, and AHU supply pressure.

[0098] · -k area of ​​AHU u u r VAV zone damper opening,

[0099] ·p u -AHU u's fan air supply pressure.

[0100] The cost function J can be defined solely based on air-side information, where the AHU fan power function and the air conditioning equipment power function are both defined on the regional regulated air mass flow rate (e.g., regulated air supply rate) and can be learned via data. The above constraint (1) is about regional thermodynamics and uses a bilinear model:

[0101]

[0102] By introducing a new variable We have

[0103]

[0104] It becomes a linear model and can be effectively identified. Ambient air conditioning load Q r (k) is considered a piecewise constant because changes in environmental conditions are a slow process compared to regional thermodynamics. The regional temperature for subsequent time periods can be defined as a linear function of the regional temperature for the current time period within the forecast range, the regional air conditioning load, the mass flow rate of the regulated air supply in the corresponding region among multiple regions, and the temperature of the regulated air.

[0105] The constraint (2) above pertains to the thermal comfort settings for occupants, such as a predetermined range, for example, 23°C to 26°C during working hours and 28°C to 30°C at night. Occupants can input their thermal preferences online, or machine learning methods can be used to learn specific occupant thermal preferences based on time, environmental settings, and the occupant's physical activity. Zone CO2 measurement and zone occupancy can be determined via machine learning prediction models. If the zone is unoccupied, the thermal settings for that zone can simply be set, for example, 28°C to 30°C; otherwise, they can be set to 23°C to 26°C.

[0106] The constraint (3) above concerns the coupling of regional cold air supply caused by the shared AHU fan and duct network. The coupling function h can be defined based on a standard fluid dynamics model that depends on prior knowledge related to the duct network layout information.

[0107] According to various non-limiting embodiments, regional air quality indicators may include regional carbon dioxide (CO2) concentration data. Regional CO2 concentration data for subsequent time periods within the prediction range may be determined as a multi-component function by a dynamic model of CO2 concentration, the multi-component function including multiple components related to regional parameters selected from a group of air volume, air density, occupant and / or equipment CO2 production rates for the respective regions.

[0108] In multiple regions (e.g., region 1, ..., region i (or r related to constraints (1) to (3)), ..., region n), the regional air quality index (e.g., CO2 concentration) W at discrete time interval (k+1) is... i The (k+1) prediction model is described below. Assume that the air quality index (e.g., fresh air CO2 concentration) W at each time interval k is... o (k)ppm is known.

[0109] in,

[0110] ·W i (k) - CO2 concentration in region i within discrete time interval k.

[0111] ·W s (k) - The concentration of CO2 in the air after adjustment within the discrete time interval k.

[0112] ·B i (k) - The CO2 generation rate of occupants and devices in region i within a discrete time interval k.

[0113] ·V i - Air volume in zone i

[0114] ·ρ - air density, and

[0115] · - The mass flow rate of regulated air in region i within a discrete time interval k.

[0116] Make d r (k)∈[0,1] represents the percentage of return air used for recirculation of the air conditioning system within a discrete time interval k (e.g., return air ratio). Assuming no air loss in the duct network (otherwise, a compensation term would be required), the CO2 concentration in the conditioned air at the air conditioning unit outlet (e.g., outlet coil) is determined by the CO2 concentrations of the return air and fresh air, respectively:

[0117]

[0118] According to various non-limiting embodiments, the air quality index W is adjusted. s (k) The air quality index W of the return air i (k) Air quality index W of fresh air o (k) and return air ratio d r (k) Determined.

[0119] Assume the setpoint for the CO2 concentration in region i within the discrete time interval k is [W]. i,L W i,H The following is given:

[0120] Among them, [W i,L W i,H [] represents the minimum and maximum CO2 concentrations required, i.e., the air quality requirements for region i. The constraints (1) to (6) above allow users to achieve high energy savings in HVAC control while ensuring heat and air quality constraints.

[0121] Figure 4 This is a block diagram illustrating an example method 400 for scheduling an HVAC system according to various embodiments of the present disclosure. The HVAC system may include air conditioning unit 401 and AHU 1 to AHU m. Method 400 may include the features of method 100.

[0122] Method 400 may include three calculation phases: Phase 1 410, Phase 2 420, and Phase 3 430. Phase 1 410 may include steps in regions 1...n of multiple regions served by AHU 1... and regions 1...n of multiple regions served by AHU m. Phase 2 420 may include steps across AHU 1, ... and AHU m and air conditioning unit 401. Phase 3 430 may include steps in regions 1...n of multiple regions served by AHU 1... and regions 1...n of multiple regions served by AHU m.

[0123] Phase 1 410

[0124] In phase 1 410, the minimum regulated air supply rate and return air ratio of each of AHU 1 to AHU m will be determined simultaneously or continuously.

[0125] According to various non-limiting embodiments, the air conditioning loads in multiple regions associated with the relevant AHU can be summed as the cost function, and the following constraints can be considered in region i:

[0126] (a) Thermodynamics of each region i (e.g., region temperature),

[0127] (b) The thermal comfort range for each zone i (e.g., zone temperature setpoint).

[0128] (c) CO2 concentration dynamics for each region i (e.g., regional air quality indicators), and

[0129] (d) The permissible CO2 concentration range for each region i (e.g., the region air quality setpoint).

[0130] Assuming the initial region temperature T i (0), Initial CO2 concentration W in the region i (0) Regional environmental air conditioning load generation function v i (k) Regional CO2 production rate function B i (k), and the fresh air CO2 concentration function W o (k) are all known. District air conditioning supply rate. Compared to AHU return air d r (k) can be determined in stage 1 410 by the following algorithm (7).

[0131]

[0132]

[0133]

[0134]

[0135]

[0136] in,

[0137] ·c p -Specific heat capacity of air

[0138] η(k) - Efficiency of the air conditioning equipment within discrete time k.

[0139] ·T oa -The temperature of the fresh air,

[0140] ·T c - Adjusted air temperature

[0141] ·T i (k) - Temperature of region I within discrete time k,

[0142] • α1 and α2 - constants for each prediction range (e.g., adjustable for different prediction ranges).

[0143] Assume the temperature setpoint [T] in region i within the discrete time interval k. i,l ,T i,h As given in constraint (4) above, where, [T i,l ,T i,h ] represents the minimum and maximum zone temperatures required in zone i.

[0144] The algorithm (7) described above includes constraints (2), (4), (5), and (6). For example, a user can replace constraints (4) to (6) with another dynamic model (e.g., a dynamic model of CO2 concentration for each region), and the same three-level architecture can still work, as long as the output is a region-regulated air supply rate that ensures thermal comfort and CO2 concentration in each region.

[0145] In Table 1 below, for an AHU including 100 regions and a sampling period of 15 minutes, calculating the 8-step prediction range takes only 4.98 seconds. For a 32-step prediction range, the calculation time can be increased to 26.37 seconds, which is more than sufficient for practical applications.

[0146] Table 1. Calculation time for 8-step and 32-step prediction ranges

[0147] Number of regions Calculation time (2 hours) Calculation time (8 hours) 10 0.58 seconds 2.62 seconds 20 1.03 seconds 4.55 seconds 50 3.38 seconds 12.56 seconds 100 4.98 seconds 26.37 seconds 150 7.51 seconds 32.96 seconds 200 10.33 seconds 51.20 seconds

[0148] The number of service areas. The second column of Table 1 shows the computation time for an 8-step prediction range. The third column of Table 1 shows the computation time for a 32-step prediction range. Each step represents a 15-minute sampling period, so an 8-step prediction range represents a 2-hour time period. Similarly, a 32-step prediction range represents an 8-hour time period.

[0149] Phase Two 420

[0150] According to various non-limiting embodiments, in phase two 420, the zone air supply rate determined from phase one 410 can be fixed, and the return air ratio in the AHU can be adjusted. This can substantially adjust the air conditioning load of the AHU by increasing or decreasing the fresh air supply, and thus adjust the building air-side air conditioning load sensed at the air conditioning unit side, which ultimately affects the air conditioning unit efficiency. More specifically, assuming that the air conditioning unit efficiency coefficient of performance (COP) is known and denoted by η(k), this can be expressed as a function of the air-side air conditioning load as described below.

[0151] According to various non-limiting embodiments, two metrics can be minimized: the first metric is the total air conditioning unit energy consumption calculated as the product of COP and air-side air conditioning load; the second metric is the difference in fresh air supply rates between any two AHUs—that is, the CO2 concentration of the supplied fresh air in each AHU should be similar. The first metric is crucial for energy saving. The second metric is considered from the tenant's perspective but is not important for energy saving. Users can add any other optimization metrics, as long as the metric can be expressed as a function of the AHU air conditioning load.

[0152] According to various non-limiting embodiments, in stage two 420, method 400 may include (i) obtaining parameters related to the coefficient of performance (COP) of the air conditioning equipment; (ii) determining the COP-related parameters of the air conditioning equipment as a first parameter if the air conditioning load associated with at least one AHU is less than or equal to a first predetermined threshold; (iii) determining the COP-related parameters of the air conditioning equipment as a second parameter if the air conditioning load associated with at least one AHU is less than or equal to a second predetermined threshold and greater than or equal to the first predetermined threshold; and (iv) continuing the steps as described in (iii) until the air conditioning load associated with at least one AHU is greater than a final predetermined threshold, and determining the COP-related parameters of the air conditioning equipment as a final parameter. This may mean that the COP-related parameters of the air conditioning equipment (e.g., η(k), the efficiency of the air conditioning equipment within a discrete time interval k) vary according to the air conditioning load associated with AHU 1 to AHU m. When the air conditioning load is too high or too low, the efficiency of the air conditioning equipment may decrease.

[0153] According to various non-limiting embodiments, method 400 may further include an optimization function based on parameters determined in relation to the performance coefficient of the air conditioning equipment and optimization of the return air ratio based on the difference between the return air ratio and the average return air ratio when the damper opening is at each position.

[0154] According to various non-limiting embodiments, AHU 1 to AHU m (including AHU 1, ..., AHU j, AHU m) may each include a damper opening for changing the return air ratio by adjusting the position of the damper opening. The average return air ratio between these positions of the damper opening can be determined. And the difference between the return air ratio and the average return air ratio at each of these damper opening positions. Therefore, n representing the number (e.g., m) of AHUs can be determined. a .

[0155] Therefore, according to various non-limiting embodiments, the AHU damper opening (i.e., the return air ratio in the AHU) can be adjusted, for example, from 0% to 100%, and can be maintained at the minimum regulated air supply rate determined in stage 410 to ensure that the air quality requirements in each AHU are met. and Q j (k) represents the maximum and minimum allowable air conditioning loads associated with AHU j, j∈(1,…,m), where, When the supply is at the same regulated air supply rate calculated in stage 1 410, it is determined by setting the return air ratio to 0; and Q j (k) Determined based on the return air ratio and the cold air supply rate from stage 410. The actual selected air conditioning load Q is then determined. j (k) The upper and lower limits must not be exceeded. In various embodiments, the minimum regulated air supply rate determined in phase 410 may include a range of minimum regulated air supply rates, and maintaining the minimum regulated air supply rate may include maintaining the minimum regulated air supply rate within that range. The minimum regulated air supply rate determined in phase 410 may be optimized while remaining within that range. Method 400 may include setting a lower limit (e.g., Q) of the air conditioning load associated with at least one AHU. j (k)) and upper limit (e.g., Q) j (k)). The air conditioning load can be set at the lower limit (e.g., Q). j (k)) and upper limit (e.g., Q) j The minimum regulated air supply rate is set between (k) and (k), within which the minimum regulated air supply rate is located. Alternatively, a lower limit can be set when the return air ratio is at its maximum, and an upper limit when the return air ratio is zero. The air conditioning load can also be set between the lower and upper limits.

[0156] Therefore, according to various non-limiting embodiments, the amount of fresh air can be optimized by changing the damper opening (e.g., changing the air conditioning load). Thus, the return air ratio, i.e., the ratio of the amount (e.g., volume) of return air to the amount (e.g., volume) of conditioned air (i.e., the mixture of fresh air and return air), can be optimized.

[0157] An algorithm for determining the air conditioning load is proposed (8). For example, some special AHUs may not allow changes to the damper opening. In this case, the user can simply set the actual AHU air conditioning load to the lower limit of the allowable limit, i.e., Q. j (k)= Q j (k).

[0158]

[0159]

[0160]

[0161]

[0162]

[0163]

[0164]

[0165] Since the number of AHUs is relatively small, the above algorithm (8) can be efficiently converted into a standard mixed-integer linear program (MILP), which can be solved efficiently. Table 2 below shows that, for example, for a building with 100 AHUs and each AHU serving 100 areas (in row 5), the calculation for stage two can be completed in just 0.2 seconds, which optimizes the efficiency of the air conditioning equipment.

[0166] Table 2 Calculation time for the 32-step prediction range

[0167] AHU quantity Number of regions per AHU Calculation time (8 hours) 10 100 0.13 seconds 20 100 0.15 seconds 50 100 0.18 seconds 100 100 0.2 seconds

[0168] The first column of Table 2 shows the number of AHUs connected to and thus supplying conditioned air to the air conditioning unit. The second column of Table 2 shows the number of areas served by each AHU. The third column of Table 2 shows the calculation time for the 32-step prediction range.

[0169] Phase 3 430

[0170] According to various non-limiting embodiments, in order to improve the air coupling function h described in constraint (3), a machine learning-based method was developed that maps the AHU fan supply pressure and VAV zone damper opening to the zone-regulated air supply rate. Essentially, instead of using the above constraint (3), the following constraint (3') is learned:

[0171]

[0172] like Figure 5 The simple multilayer neural network shown was used to learn the function h on a list of data obtained from the testbed. The results show a prediction accuracy of 90%.

[0173] Since a new constraint (3') is introduced to capture the AHU-regional regulated air coupling, the energy cost function J is updated as shown in (9):

[0174]

[0175] Limited by

[0176]

[0177] From Phase Two:

[0178] Output:

[0179] By introducing an efficient metaheuristic algorithm, as shown in Algorithm (9), this stage 3430 can be solved in 2 minutes, while the accuracy decreases by 3.58% relative to the best possible solution (see Table 3, row 5, column 6). The experimental results are shown in Table 3 below, which includes the air-coupled model derived from machine learning.

[0180] Table 3. Calculation time and inaccuracy rate of the 4-hour forecast range

[0181]

[0182] *H p - Forecast range, i.e., 4 hours; H W A 15-minute forecast window, i.e., 16 forecast windows × 15 minutes = 4 hours.

[0183] Method 400 may further include transmitting the optimized return air ratio to a scheduler (e.g., scheduler 230); the scheduler receiving the optimized return air ratio and energy efficiency data of the air conditioning equipment; balancing parameters related to the optimized return air ratio and the coefficient of performance of the air conditioning equipment over a subsequent time period; calculating an air supply strategy based on the balance, the air supply strategy including regulated air supply allocation for multiple zones over a subsequent time period to minimize the energy consumption of the air conditioning equipment while meeting zone setpoints; and transmitting the air supply strategy to the multiple zones.

[0184] The HVAC scheduling method 400 described in this disclosure allows for a distributed computing architecture, where Phase 1 (410) and Phase 3 (430) computations can be performed on several servers, each responsible for only a small number of AHUs and associated areas. Phase 2 can be performed on a single server. The number of servers required for Phase 1 and Phase 3 computations can be determined by the number of AHUs. For example, currently, based on a standard laptop computer, the proposed computation algorithm requires 2.5 minutes per AHU. If one server can process x AHUs simultaneously while meeting the maximum allowed computation time, such as 2.5 minutes per AHU, then to process y AHUs simultaneously, we need y / x servers. All servers processing a single AHU may need to communicate with the server responsible for Phase 2 optimization.

[0185] Reference Figures 6 to 8 Describe further experimental results.

[0186] Figure 6 Figure 610 depicts a comparison of actual temperature with estimated temperature using a regional environment model with constraints (1) to (3) according to various embodiments of the present disclosure, and Figure 620 depicts the error between actual temperature and estimated temperature. Figure 6 The actual temperature is shown to be quite close to the temperature estimated by the regional environmental model.

[0187] Figure 7 This is a graph showing a comparison between measured values ​​of CO2 concentration according to various embodiments of the present disclosure and estimated values ​​using a regional environmental model with constraints (4). When the regional occupancy status remains unchanged, the CO2 production rate B... i (k) Constant. Figure 701 shows the measured CO2 concentration data, and Figure 702 shows the estimated CO2 concentration data through a regional environmental model with constraints (4). Figure 7 The measured CO2 concentration data is shown to be close to the CO2 concentration data estimated by the regional environmental model.

[0188] Figure 8Figure 801 shows the experimental results for stage 3, 430. Figure 801 depicts the convergence curves for 100 regions. The proposed metaheuristic algorithm can converge to sufficiently good results in a small number of iterations in a more efficient and effective manner.

[0189] Figure 9 This is a block diagram illustrating an example electronic device 900 according to an embodiment of the present disclosure. The electronic device 900 may be a laptop computer, desktop computer, tablet computer, automotive computer, smartphone, personal digital assistant, server, or other electronic device capable of running computer applications. In some embodiments, the electronic device 900 includes a processor 902, an input / output (I / O) module 904, a memory 906, a power supply unit 908, and one or more network interfaces 910. The electronic device 900 may include additional components. In some embodiments, the processor 902, the input / output (I / O) module 904, the memory 906, the power supply unit 908, and the network interface 910 are housed together in a common housing or other component.

[0190] Processor 902 can execute instructions, such as generating output data based on data input. Instructions may include programs, code, scripts, modules, or other types of data stored in memory (e.g., memory 906). Additionally or alternatively, instructions may be encoded as pre-programmed or reprogrammable logic circuits, logic gates, or other types of hardware or firmware components or modules. Processor 902 may be or may include a multi-core processor with multiple cores, each of which may have an independent power domain and may be used to enter and exit different operating or performance states based on workload. Additionally or alternatively, processor 902 may be or may include a general-purpose microprocessor, a dedicated coprocessor, or another type of data processing device. In some cases, processor 902 performs high-level operations of electronic device 900. For example, processor 902 may be used to execute or interpret software, scripts, programs, functions, executable files, or other instructions stored in memory 906.

[0191] Example I / O module 904 may include a mouse, keypad, touch screen, scanner, optical reader, and / or stylus (or other input device) through which a user of electronic device 900 can provide input to electronic device 900, and may also include one or more audio speakers for providing audio output and video display devices for providing text, audiovisual and / or graphic output.

[0192] Example memory 906 may include a computer-readable storage medium, such as a volatile memory device, a non-volatile memory device, or both. Memory 906 may include one or more read-only memory devices, random access memory devices, buffer memory devices, or combinations of these and other types of memory devices. In some cases, one or more components of the memory may be integrated with or otherwise associated with another component of the electronic device 900. Memory 906 may store instructions executable by processor 902. In some examples, memory 906 may store instructions for operating system 912 and for application program 914. Memory 906 may also store a database 916.

[0193] Example power supply unit 908 provides power to other components of electronic device 900. For example, other components may operate based on power supplied by power supply unit 908 via a voltage bus or other connection. In some embodiments, power supply unit 908 includes a battery or battery system, such as a rechargeable battery. In some embodiments, power unit 908 includes an adapter (e.g., an AC adapter) that receives an external power signal (from an external source) and converts the external power signal into an internal power signal regulated for the components of electronic device 900. Power unit 908 may include other components or operate in other ways.

[0194] Electronic device 900 can be used to operate in wireless, wired, or cloud network environments (or combinations thereof). In some embodiments, electronic device 900 can access a network using network interface 910. Network interface 910 may include one or more adapters, modems, connectors, sockets, terminals, ports, slots, etc. The wireless network to which electronic device 900 accesses can operate, for example, according to a wireless network standard or another type of wireless communication protocol. For example, the wireless network can be used to operate as a wireless local area network (WLAN), a personal area network (PAN), a metropolitan area network (MAN), or other types of wireless networks. Examples of WLAN include networks operating according to one or more of the 802.11 family of standards developed by IEEE (e.g., Wi-Fi networks). Examples of PAN include networks operating according to short-range communication standards (e.g., Networks that operate via near-field communication (NFC), ZigBee, millimeter-wave communication, etc. The wired network to which the electronic device 900 connects can include, for example, Ethernet, SONET, circuit-switched networks (e.g., using components such as SS7, cables, etc.).

[0195] Some of the subjects and operations described in this specification can be implemented in digital electronic circuits, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations thereof. Some of the subjects described in this specification can be implemented as one or more computer programs, that is, one or more modules of computer program instructions encoded on a computer storage medium for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. The computer storage medium can be or can be included in a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination thereof. Furthermore, although the computer storage medium is not a propagating signal, it can be a source or destination of computer program instructions encoded in an artificially generated propagating signal. The computer storage medium can also be or be included in one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices).

[0196] Some of the operations described herein can be implemented as operations performed by a data processing device on data stored on one or more computer-readable storage devices or received from other sources.

[0197] The term "data processing apparatus" encompasses all types of apparatus, devices, and machines for processing data, including, for example, programmable processors, computers, systems-on-a-chip, or a combination thereof. The apparatus may include special-purpose logic circuitry, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs). In addition to hardware, the apparatus may also include code that creates an execution environment for the computer program in question, such as code constituting processor firmware, protocol stacks, database management systems, operating systems, cross-platform runtime environments, virtual machines, or combinations thereof.

[0198] A computer program (also known as a program, software, software application, script, or code) can be written in any programming language, including compiled or interpreted languages, declarative or procedural languages, and can be deployed in any form, including as a standalone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but does not necessarily, correspond to a file in a file system. A program may be stored as a part of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), as a single file dedicated to that program, or as multiple coordinating files (e.g., a file storing one or more modules, subroutines, or code sections). A computer program can be deployed to execute on a single computer or on multiple computers located at a site or distributed across multiple sites and interconnected by a communication network.

[0199] Some of the processes and logical flows described herein can be executed by one or more programmable processors, which execute one or more computer programs to perform actions by manipulating input data and generating outputs. Processes and logical flows can also be executed by special-purpose logic circuits (e.g., field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs)), and devices can also be implemented as special-purpose logic circuits.

[0200] While this specification includes numerous details, these should not be construed as limiting the scope of claims, but rather as descriptions of features specific to particular examples. Certain features described herein or illustrated in the accompanying drawings in the context of individual embodiments may also be combined. Conversely, various features described or illustrated in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0201] Similarly, although operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring such operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single product or packaged into multiple products.

[0202] Several embodiments have been described. However, it should be understood that various modifications can be made. Therefore, other embodiments are within the scope of the appended claims.

Claims

1. A method for scheduling a heating, ventilation, and air conditioning (HVAC) system, wherein, The HVAC system includes an air conditioning plant, at least one air handling unit (AHU) connected to the air conditioning plant, and the at least one air handling unit is configured to serve a plurality of zones, the method comprising: obtaining zone environment information including zone temperatures, zone air quality indicators, and zone setpoints for the plurality of zones including zone temperature setpoints and zone air quality setpoints; obtaining conditioned air temperature and conditioned air quality indicators of conditioned air associated with the at least one air handling unit, and fresh air temperature of fresh air mixed with return air to form pre-conditioned air with the conditioned air; determining, for the at least one air handling unit and a prediction horizon, a minimum conditioned air supply rate and a return air ratio based on a conditioned air function of parameters including the zone temperatures, the conditioned air temperature, the fresh air temperature, the zone air quality indicators, and the conditioned air quality indicators, so as to collectively satisfy the zone setpoints for the plurality of zones, wherein the at least one air handling unit includes a damper opening, the damper opening being configured to change the return air ratio by adjusting a position of the damper opening, the method further comprising: determining an average return air ratio between the positions of the damper opening, and determining a difference between the return air ratio when the damper opening is at each of the positions and the average return air ratio, optimizing the return air ratio based on an optimization function of determined parameters related to a coefficient of performance of the air conditioning plant and the difference between the return air ratio when the damper opening is at each of the positions and the average return air ratio.

2. The method of claim 1, wherein, the conditioned air quality indicators are determined from air quality indicators of the return air, air quality indicators of the fresh air, and the return air ratio.

3. The method of claim 1 or 2, wherein, the zone air quality indicators include zone carbon dioxide (CO2) concentration data, wherein the zone carbon dioxide (CO2) concentration data for subsequent time periods within the prediction horizon are determined as a multi-component function from zone parameters selected from a group of air volume, air density, occupant, and / or equipment carbon dioxide (CO2) generation rates of respective zones of the plurality of zones.

4. The method of claim 1 or 2, wherein, the zone temperature for subsequent time periods is defined as a linear function of the zone temperature for a current time period within the prediction horizon, zone air conditioning loads, mass flow rates of conditioned air supply of respective zones of the plurality of zones, and the conditioned air temperature.

5. The method of claim 1, further comprising: setting a lower limit and an upper limit of air conditioning loads associated with the at least one air handling unit, wherein the lower limit is set when the return air ratio is maximum, and the upper limit is set when the return air ratio is zero, wherein the air conditioning loads are set between the lower limit and the upper limit.

6. The method of claim 5, further comprising: (i) obtaining a coefficient of performance related parameter of the air conditioning plant; (ii) if the air conditioning load associated with the at least one air handling unit is less than or equal to a first predetermined threshold, determining the coefficient of performance related parameter of the air conditioning plant as a first parameter; (iii) if the air conditioning load associated with the at least one air handling unit is less than or equal to a second predetermined threshold and greater than or equal to the first predetermined threshold, determining the coefficient of performance related parameter of the air conditioning plant as a second parameter; and (iv) continuing the steps as in (iii) until the air conditioning load associated with the at least one air handling unit is greater than a last predetermined threshold and determining the coefficient of performance related parameter of the air conditioning plant as a last parameter.

7. The method of claim 1, further comprising: mapping a conditioned air coupling to a mass flow rate of conditioned air supply for the plurality of zones based on zone damper positions of the plurality of zones and fan supply air pressure of the at least one air handling unit.

8. The method of claim 7, further comprising: communicating the optimized return air ratio to a dispatcher; the dispatcher receiving the optimized return air ratio and energy efficiency data of the air conditioning plant; balancing the optimized return air ratio with the coefficient of performance related parameter of the air conditioning plant for a subsequent time period; calculating an air supply strategy based on the balancing, the air supply strategy comprising an allocation of conditioned air supply for the plurality of zones in the subsequent time period to minimize energy consumption of the air conditioning plant while satisfying the zone setpoints; and communicating the air supply strategy to the plurality of zones.

9. A system for scheduling a heating, ventilation, and air conditioning (HVAC) system, wherein, The HVAC system comprises an air conditioning plant, at least one air handling unit (AHU) connected to the air conditioning plant, the at least one air handling unit for serving a plurality of zones, the system comprising: a zone module for obtaining zone environment information, the zone environment information comprising zone temperatures, zone air quality indicators, and zone setpoints of the plurality of zones, the zone setpoints of the plurality of zones comprising zone temperature setpoints and zone air quality setpoints; an input module for obtaining conditioned air temperature and conditioned air quality indicators of conditioned air associated with the at least one air handling unit, and for fresh air temperature of fresh air mixed with the conditioned air to form pre-conditioned air; and a dispatcher for determining, for the at least one air handling unit and a prediction horizon, a minimum conditioned air supply rate and a return air ratio based on a conditioned air function of parameters comprising the zone temperatures, the conditioned air temperature, the fresh air temperature, the zone air quality indicators, and the conditioned air quality indicators, so as to collectively satisfy the zone setpoints of the plurality of zones, wherein the at least one air handling unit comprises a damper position, the damper position for varying the return air ratio by adjusting the damper position, the dispatcher further for: determining an average return air ratio between the positions of the damper opening, and determining a difference between the return air ratio when the damper opening is at each of the positions and the average return air ratio, optimizing the return air ratio based on an optimization function of the determined coefficient of performance related parameter of the air conditioning equipment and the difference between the return air ratio when the damper opening is at each of the positions and the average return air ratio.

10. The system of claim 9, wherein, the conditioned air mass index is determined from an air mass index of the return air, an air mass index of the fresh air, and the return air ratio.

11. The system of claim 9 or 10, wherein, the zone air mass index includes zone carbon dioxide (CO2) concentration data, wherein the zone carbon dioxide (CO2) concentration data for subsequent time periods within the prediction horizon is determined as a multi-component function from zone parameters selected from the group of air volume, air density, occupant and / or equipment carbon dioxide (CO2) generation rates of respective zones of the plurality of zones.

12. The system of claim 9 or 10, wherein, the zone temperature for subsequent time periods is defined as a linear function of the zone temperature for current time periods within the prediction horizon, zone air conditioning loads, mass flow rates of conditioned air supply of respective zones of the plurality of zones, and the conditioned air temperature.

13. The system of claim 9, wherein, the scheduler is further configured to: set a lower limit and an upper limit of an air conditioning load associated with the at least one air handling unit, wherein the lower limit is set when the return air ratio is maximum and the upper limit is set when the return air ratio is zero, wherein the air conditioning load is set between the lower limit and the upper limit.

14. The system of claim 13, wherein the input module is further configured to: (i) obtain a coefficient of performance related parameter of the air conditioning equipment; wherein the scheduler is further configured to: (ii) determine the coefficient of performance related parameter of the air conditioning equipment as a first parameter if the air conditioning load associated with the at least one air handling unit is less than or equal to a first predetermined threshold; (iii) determine the coefficient of performance related parameter of the air conditioning equipment as a second parameter if the air conditioning load associated with the at least one air handling unit is less than or equal to a second predetermined threshold and greater than or equal to the first predetermined threshold; and (iv) continue the steps as in (iii) until the air conditioning load associated with the at least one air handling unit is greater than a last predetermined threshold and determine the coefficient of performance related parameter of the air conditioning equipment as a last parameter.

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