WO2022261965A1 - Procédé et système de gestion de chauffage, de ventilation et de climatisation, et support de stockage - Google Patents

Procédé et système de gestion de chauffage, de ventilation et de climatisation, et support de stockage Download PDF

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Publication number
WO2022261965A1
WO2022261965A1 PCT/CN2021/101031 CN2021101031W WO2022261965A1 WO 2022261965 A1 WO2022261965 A1 WO 2022261965A1 CN 2021101031 W CN2021101031 W CN 2021101031W WO 2022261965 A1 WO2022261965 A1 WO 2022261965A1
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scene
bes
model
benchmark
classification strategy
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PCT/CN2021/101031
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English (en)
Chinese (zh)
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孙天瑞
周晓舟
白新
李奂轮
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西门子股份公司
西门子（中国）有限公司
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Priority to CN202180096951.1A priority Critical patent/CN117121025A/zh
Priority to PCT/CN2021/101031 priority patent/WO2022261965A1/fr
Publication of WO2022261965A1 publication Critical patent/WO2022261965A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"

Definitions

  • the invention relates to the field of building energy consumption, in particular to a management system, method and computer-readable storage medium of a HVAC system.
  • HVAC Heating, Ventilation and Air Conditioning
  • the building energy consumption digital twin system based on Building Energy Simulation (BES) is an energy-saving optimization method applied in recent years to improve the energy efficiency of existing buildings.
  • the embodiment of the present invention proposes a HVAC system management method, and on the other hand, proposes a HVAC system management system and a computer-readable storage medium to improve the BES model in Accuracy in energy performance prediction, which facilitates building energy optimization.
  • An HVAC management method proposed in an embodiment of the present invention includes: determining the current scene category according to the acquired current scene data related to the operation of the HVAC system and a predetermined benchmark scene classification strategy; Activate the benchmark BES model corresponding to the current scene category in the building energy consumption simulation benchmark BES model corresponding to each scene category to obtain the current benchmark BES model; use the current scene data and the current benchmark BES model to determine the HVAC system An optimal control strategy; controlling corresponding equipment in the HVAC system according to the optimal control strategy.
  • the method further includes: collecting a set number of historically collected historical reference data related to the HVAC system; the set number is greater than a set number threshold; using the historical reference data to In an initial BES model, the parameters irrelevant to the scene are calibrated; the current scene classification strategy is determined according to the historical reference data; according to the current scene classification strategy, the historical reference data is divided into different scene categories, and each The reference data of a scene category calibrates the scene-related parameters in the initial BES model to obtain a candidate BES model corresponding to the scene category; the scene classification strategy is determined as the benchmark scene classification strategy, and the A candidate BES model is determined to be the reference BES model.
  • the scene classification strategy before determining the scene classification strategy as the benchmark scene classification strategy and determining the candidate BES model as the benchmark BES model, further comprising: according to the number of scene categories, the scene category Any one or any combination of the degree of discrimination and the simulation accuracy of the candidate BES model corresponding to each scene category, comprehensively evaluate the scene classification strategy; when the comprehensive evaluation results meet the set requirements, execute the will The scene classification strategy is determined as the benchmark scene classification strategy, and the candidate BES model is determined as the operation of the benchmark BES model; otherwise, when the comprehensive evaluation result does not meet the set requirements, the set strategy is used to optimize the algorithm Optimizing the scene classification strategy, using the optimized scene classification strategy as the current scene classification strategy, and returning to execute the method of dividing the historical reference data into different scene categories according to the current scene classification strategy operate.
  • the initial BES model is a BES model established by using known fixed information related to the operation of the HVAC system and an initial estimate of uncertain information, or is a currently available BES model.
  • determining the current scene classification strategy according to the historical reference data before determining the current scene classification strategy according to the historical reference data, it further includes: determining whether there is a historical benchmark scene classification strategy, and if there is a historical benchmark scene classification strategy, then according to the historical The reference data and the historical benchmark scene classification strategy are used to determine the current scene classification strategy. If there is no historical benchmark scene classification strategy, the operation of determining the current scene classification strategy according to the historical reference data is performed.
  • it further includes: displaying information related to the HVAC system; the information related to the HVAC system comes from the current scene data and/or the control information of the HVAC system and/or or status information.
  • the scene data includes at least one or any combination of the following information: weather information, holiday information, local event information, sensor collection related to the HVAC system and/or building thermal performance in the building Information related to the HVAC system manually entered in the building; the historical reference data includes historical scene data.
  • the management system of the HVAC system proposed in the embodiment of the present invention includes: at least one memory and at least one processor, wherein: the at least one memory is used to store computer programs; the at least one processor is used to call the The computer program stored in at least one memory enables the device to perform corresponding operations, the operations including: determining the current scene category according to the acquired current scene data related to the operation of the HVAC system and a predetermined benchmark scene classification strategy ; Activate the benchmark BES model corresponding to the current scene category from the predetermined building energy consumption simulation benchmark BES model corresponding to each scene category to obtain the current benchmark BES model; use the current scene data and the current benchmark BES model to determine Find the optimal control strategy of the HVAC system; control the corresponding equipment in the HVAC system according to the optimal control strategy.
  • the operations further include: collecting a set number of historically collected historical reference data related to the HVAC system; the set number is greater than a set number threshold; utilizing the historical reference data Calibrating the parameters irrelevant to the scene in an initial BES model; determining the current scene classification strategy according to the historical reference data; according to the current scene classification strategy, dividing the historical reference data into different scene categories, using The reference data of each scene category calibrates the parameters relevant to the scene in the initial BES model to obtain a candidate BES model corresponding to the scene category; the scene classification strategy is determined as the benchmark scene classification strategy, and the The candidate BES model is determined as the reference BES model.
  • before determining the scene classification strategy as the benchmark scene classification strategy and determining the candidate BES model as the benchmark BES model further include: according to the number of scene categories, the relationship between scene categories Any one or any combination of the degree of discrimination and the simulation accuracy of the candidate BES models corresponding to each scene category, comprehensively evaluate the scene classification strategy; when the comprehensive evaluation results meet the set requirements, perform the scene classification The classification strategy is determined as the benchmark scene classification strategy, and the candidate BES model is determined as the operation of the benchmark BES model; otherwise, when the comprehensive evaluation result does not meet the set requirements, the set strategy optimization algorithm is used to The scene classification strategy is optimized, and the optimized scene classification strategy is used as the current scene classification strategy, and the operation of dividing the historical reference data into different scene categories according to the current scene classification strategy is returned.
  • the operation before determining the current scene classification strategy according to the historical reference data, the operation further includes: determining whether there is a historical benchmark scene classification strategy, and if there is a historical benchmark scene classification strategy, then according to The historical reference data and the historical benchmark scene classification strategy determine the current scene classification strategy, and if there is no historical benchmark scene classification strategy, the operation of determining the current scene classification strategy according to the historical reference data is performed.
  • the operations further include: displaying information related to the HVAC system; the information related to the HVAC system comes from the current scene data, and/or the control of the HVAC system information and/or status information.
  • Yet another HVAC system management system includes: a data collection module, used to collect current scene data related to the operation of the HVAC system; a scene decision module, used to The data and the predetermined benchmark scene classification strategy determine the current scene category of the building; the model determination module is used to activate the benchmark corresponding to the current scene category from the predetermined benchmark building energy consumption simulation BES model corresponding to each scene category BES model, to obtain the current benchmark BES model; a control strategy determination module, used to call the current benchmark BES model based on the set optimal control algorithm and the current scene data, and determine the HVAC according to the output of the current benchmark BES model An optimal control strategy of the system; a system control module, configured to control corresponding equipment in the HVAC system according to the optimal control strategy.
  • it further includes: a data recording module, which is used to record historical reference data related to the HVAC system of a set number of groups historically collected by the data collection module and/or other external modules; the setting The quantity is greater than a set quantity threshold; the first calibration module is used to calibrate the scene-independent parameters in an initial BES model by using the historical reference data; the classification strategy determination module is used to determine the current The scene classification strategy; the data division module is used to divide the historical reference data into different scene categories according to the current scene classification strategy; the second calibration module is used to use the reference data of each scene category to classify all The parameters related to the scene in the initial BES model are calibrated to obtain the candidate BES model corresponding to the scene category; the evaluation module is used for according to the number of scene categories, the degree of discrimination between the scene categories, and the candidate corresponding to each scene category Any one or any combination of the simulation accuracy of the BES model comprehensively evaluates the scene classification strategy; the result judgment module is used to determine the scene classification strategy as a benchmark when the comprehensive evaluation result meets
  • a computer-readable storage medium proposed in an embodiment of the present invention has a computer program stored thereon; the computer program can be executed by a processor and implement the management method of the HVAC system described in any one of the above implementation manners.
  • multiple scene categories of buildings are determined according to a large amount of historical reference data collected historically, and a corresponding BES model is set corresponding to each scene category.
  • the current scene category can be determined according to the currently collected scene data, and the BES model corresponding to the current scene category can be called to determine the optimal control strategy of the HVAC system, and then the HVAC system can be controlled based on the optimal control strategy
  • the corresponding equipment in the building such as heating equipment, ventilation equipment and air conditioning equipment, etc., thereby improving the accuracy of the BES model in energy performance prediction, which is conducive to the optimization of building energy conservation.
  • Fig. 1 is an exemplary flow chart of a management method for an HVAC system in an embodiment of the present invention.
  • Fig. 2 is an exemplary flowchart of a method for establishing and updating a benchmark scene classification strategy and a benchmark BES model corresponding to each scene category in an example of the present invention.
  • Fig. 3 is a schematic diagram of the process of classifying historical reference data, calibrating an initial BES model and obtaining candidate BES models corresponding to each scene category in an example of the present invention.
  • Fig. 4 is a schematic structural diagram of a management system of an HVAC system in an embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of a system for establishing and updating a benchmark scene classification strategy and a benchmark BES model corresponding to each scene category in an embodiment of the present application.
  • Fig. 6 is a schematic structural diagram of another management system of an HVAC system in an embodiment of the present application.
  • the building energy simulation (Building Energy Simulation, BES) is usually applied in the building operation stage under the influence of an external environment The impact of HVAC operating parameters is predicted.
  • BES models can generally be divided into rule-driven models and data-driven models.
  • Data-driven BES models utilize surveillance data in buildings to generate models capable of predicting system behavior, and the accuracy of the models largely depends on the quality and quantity of available training data.
  • Rule-driven models based on heat balance equations and data-driven models can provide the most detailed predictions of building performance and accurate assessments of control strategies. Since hundreds of input parameters are used in rule-driven BES models, some of which are often unmeasured, significant discrepancies can be found between the building energy consumption predicted by the BES model and the actual metered building energy consumption. Therefore, it is necessary to use the monitoring data during the building operation to calibrate and test the BES model.
  • operators Considering the impact of the external environment on the performance of the HVAC system, operators usually update the BES model at a fixed frequency, such as every three months, based on empirical rules.
  • the external information such as weather information, holiday information, and local events, etc.
  • building status information such as information collected by sensors, manual input information in the building, etc.
  • the current scene category can be determined according to the currently collected external information and building status information, and the BES model corresponding to the current scene category can be called to determine the optimal control strategy of the HVAC system, and then control based on the optimal control strategy Corresponding equipment in the HVAC system, such as heating equipment, ventilation equipment and air conditioning equipment.
  • Fig. 1 is an exemplary flow chart of a management method for an HVAC system in an embodiment of the present invention. As shown in Figure 1, the method in this embodiment may include the following steps:
  • Step 101 Determine the category of the current scenario according to the acquired current scenario data related to the operation of the HVAC system and a predetermined benchmark scenario classification strategy.
  • the scenario data related to the operation of the HVAC system may include: building status data from inside the building and/or external data from outside the building.
  • the building status data from the building may include: information collected by sensors in the building related to the HVAC system and/or thermal performance of the building; and manually input information related to the HVAC system in the building.
  • the information collected by the sensor may include: the temperature of chilled water/hot water, the power consumption of the refrigerator/heater, the flow rate of the fan coil unit (Fan Control Unit, FCU) terminal, and the indoor temperature and humidity.
  • the manually entered information may include: room comfort requirements, etc.
  • External data from outside the building may include: weather information, holiday information, local event information, etc. In practical applications, the specific scene data can be determined according to actual needs, which is not limited here.
  • a large number of groups of historical scene data can be used as input samples, and the scene category corresponding to each group of historical scene data can be used as output samples to train the artificial intelligence network. , and obtain a scene classification model, and then the current scene data can be used as the input of the scene classification model, and the scene classification model can directly output the corresponding scene category.
  • a scene database can also be set based on a predetermined benchmark scene classification strategy, which can store scene data for each scene category.
  • various artificial intelligence algorithms can be used, such as Algorithms such as K-means Clustering (KMeans) or Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) determine the scene category corresponding to the current scene data from the scene database.
  • Algorithms such as K-means Clustering (KMeans) or Density-Based Spatial Clustering Algorithm with Noise (DBSCAN) determine the scene category corresponding to the current scene data from the scene database.
  • Step 102 Activate the reference BES model corresponding to the current scene category from the predetermined reference BES models corresponding to each scene category to obtain the current reference BES model.
  • a reference BES model is set corresponding to different scene categories.
  • the benchmark BES model corresponding to the scene category can be activated as a simulation model of building performance and energy consumption, thereby making the simulation result of the benchmark BES model more accurate.
  • Step 103 using the current scene data and the current benchmark BES model to determine an optimal control strategy for the HVAC system.
  • the optimal control strategy of the HVAC system can be determined based on an optimal control algorithm, such as genetic algorithm (GA) or particle swarm optimization (PSO) or a combination algorithm of GA and PSO, so as to ensure that the buildings in the current scene Performance indicators, and has the best energy-saving performance.
  • the current BES model can be invoked based on the set optimal control algorithm and the current scene data, for example, the current scene data and other different input parameters obtained based on the optimal control algorithm can be used as the current benchmark
  • the input of the BES model, and different outputs of the current benchmark BES model are obtained, and the optimal control strategy of the HVAC system is determined according to the output of the current benchmark BES model.
  • the operating parameters that need to be optimized in the optimal control strategy may be the temperature of chilled water/hot water, the air flow rate of each FCU, and the like.
  • Step 104 controlling corresponding equipment in the HVAC system according to the optimal control strategy.
  • corresponding control signals may be sent to heating, ventilation and air conditioning system equipment in the building according to the optimal control strategy.
  • information related to the HVAC system can be further displayed.
  • the information related to the HVAC system comes from the current scene data, and/or control information and/or status information of the HVAC system.
  • information related to building thermal and HVAC systems such as indoor temperature, energy consumption, HVAC equipment status, etc. can be displayed.
  • the above method can be executed periodically according to a preset time interval. Alternatively, in some applications, it can also be performed in real time.
  • FIG. 2 is an exemplary flow chart of a benchmark scene classification strategy and a method for establishing and updating a benchmark BES model corresponding to each scene category in an example of the present invention.
  • the update process may be performed periodically according to a preset time interval.
  • the method may include the following steps:
  • Step 201 collecting a set number of historically collected historical reference data related to the HVAC system; the set number is greater than a set number threshold.
  • the set quantity threshold refers to a sufficiently large value that meets requirements.
  • the historical reference data may include historical scene data and other relevant data, such as control strategies, energy consumption, and the like.
  • the details may be determined according to actual needs, and are not limited here.
  • the amount of historical reference data will also increase when the benchmark scene classification strategy and the benchmark BES model corresponding to each scene category are subsequently updated.
  • the updated benchmark scene classification strategy and the benchmark BES model corresponding to each scene category will be more and more optimized.
  • Step 202 using the historical reference data to calibrate the scene-independent parameters in an initial BES model 20 .
  • the initial BES model can use known fixed information related to the operation of the HVAC system, such as geometric information, structural information, HVAC system information, etc., and uncertain information such as HVAC equipment performance, occupancy, etc.
  • the initial valuation of the established BES model can be a currently available BES model that already exists if no major renovations to the building and HVAC systems have been undertaken.
  • the parameters irrelevant to the scene may be some structural feature data and the like.
  • Step 203 when initially establishing the benchmark scene classification strategy and the benchmark BES model corresponding to each scene category, in this step, the current scene classification strategy can be determined according to the historical reference data.
  • the current scene classification strategy can be determined directly based on the historical reference data, or the previous benchmark scene classification strategy can be referred to, that is, in the existing history
  • the current scenario classification strategy may be determined according to the historical reference data and the historical benchmark scenario classification strategy.
  • the scenario classification strategy may be determined according to actual conditions, and is not limited here.
  • classification criteria may be maximum temperature, minimum temperature, average temperature, precipitation, air pollution index, wind speed, weekdays/weekends, other parameters related to weather and/or building operation and/or combinations of these parameters.
  • Step 204 divide the historical reference data into different scene categories, use the reference data of each scene category to calibrate the scene-related parameters in the initial BES model, and obtain the corresponding Candidate BES models for the scene category.
  • FIG. 3 shows a schematic diagram of the process of classifying historical reference data, calibrating the initial BES model, and obtaining candidate BES models corresponding to each scene category.
  • the process can include:
  • the reference data 31 is divided into different scene categories based on the scene classification strategy 32, and a data subset 331 under scene category 1, a data subset 332 under scene category 2, ..., and a data subset 33n under scene category n are obtained.
  • Use the data subset under each scene category to perform model calibration 34 on the initial BES model, and obtain the candidate BES model 351 under the scene category 1, the candidate BES model 352 under the scene category 2, ..., the candidate BES under the scene category n Model 35n.
  • the current scene classification strategy and candidate BES model can meet the requirements, for example, the current scene classification strategy and candidate BES model are the scene classification strategy and candidate BES model obtained after multiple updates, then the scene can be directly classified
  • the strategy is determined as the benchmark scene classification strategy, and the candidate BES model is determined as the benchmark BES model.
  • Step 205 according to any one or any combination of the number of scene categories, the degree of discrimination between scene categories, and the simulation accuracy of candidate BES models corresponding to each scene category, comprehensively evaluate the scene classification strategy.
  • the error between the simulation results of the candidate BES models and the results of historical samples can be calculated for each candidate BES model, such as the average Error or root mean square error, etc., so as to obtain an evaluation score, and then perform comprehensive processing such as weighted summation on the evaluation scores of the candidate BES models corresponding to each scene category, so as to obtain a comprehensive score, and then the comprehensive score can be obtained Compare with a threshold to determine whether the simulation accuracy of the candidate BES model corresponding to each scene category meets the requirements.
  • Step 206 judge whether the evaluation result meets the requirement, and execute step 207 if the comprehensive evaluation result meets the set requirement; otherwise, execute step 208.
  • Step 207 determining the scene classification strategy as the benchmark scene classification strategy, and determining the candidate BES model as the benchmark BES model.
  • Step 208 optimize the scene classification strategy by using the set strategy optimization algorithm, use the optimized scene classification strategy as the current scene classification strategy, and return to step 204 .
  • an artificial intelligence algorithm such as a genetic algorithm (GA) or a particle swarm optimization algorithm (PSO) or a combined algorithm of GA and PSO can be used to optimize the scene classification strategy.
  • simulation acceleration algorithms such as surrogate models can also be used to generate optimized scene classification strategies.
  • the management method of the HVAC system in the embodiment of the present invention has been described in detail above, and the management system of the HVAC system in the embodiment of the present invention will be described in detail below.
  • the management system of the HVAC system in the embodiment of the present invention can be used to implement the management method of the HVAC system in the embodiment of the present invention.
  • Fig. 4 is an exemplary structural diagram of a management system of an HVAC system in an embodiment of the present invention.
  • the management system 41 of the HVAC system may include: a data collection module 411 , a scenario decision module 412 , a model determination module 413 , a control strategy determination module 414 and a system control module 415 .
  • the data collection module 411 is used to collect current scene data related to the operation of the HVAC system, process the collected data, and send the processed data to the digital interface of the scene decision module 412 .
  • the data collection module 411 can collect the information collected by the sensor 421 related to the HVAC system and/or the thermal performance of the building in the building 42; it can also collect the information manually input through the control panel 422 in the building 42 ; Information from external data sources 43 can also be collected, such as weather information, holiday information, local event information, and the like.
  • the scene decision module 412 is used to determine the current scene category of the building according to the current scene data and the predetermined benchmark scene classification strategy.
  • the model determination module 413 is used to activate the reference BES model corresponding to the current scene category from the predetermined reference BES models 4131 , 4132 , .
  • scenario decision module 412 the model determination module 413 and the BES models corresponding to each scenario category may be components of the digital twin system DT of the HVAC system.
  • the control strategy determination module 414 is used to call the current BES model based on the set optimal control algorithm and the current scene data, and determine the optimal control strategy of the HVAC system according to the output of the current reference BES model.
  • the system control module 415 is used to control corresponding equipment in the HVAC system 423 in the building 42 according to the optimal control strategy, such as heating equipment, ventilation equipment and air conditioning equipment.
  • the management system of the HVAC system in the embodiment of the present invention may further include a display module 416 as shown in the dotted line in FIG. model, and at least one of the system control module 415's information related to building thermal and HVAC systems, and display the information.
  • FIG. 5 is a schematic structural diagram of a system for establishing and updating a benchmark scene classification strategy and a benchmark BES model corresponding to each scene category in an embodiment of the present application.
  • the system may include: data recording module 501, first calibration module 502, classification strategy determination module 503, data division module 504, second calibration module 505, evaluation module 506, result determination module 507 and strategy optimization Module 508.
  • the data recording module 501 is used to record a set number of HVAC system-related historical reference data historically collected by the data collection module and/or other external modules; the set number is greater than a set number threshold.
  • the first calibration module 502 is used to calibrate the scene-independent parameters in an initial BES model by using the historical reference data.
  • the classification strategy determination module 503 is used to determine the current scenario classification strategy according to the historical reference data.
  • the classification strategy determination module 503 may further refer to the historical benchmark scenario classification strategy, for example, may determine the current scenario classification strategy according to the historical reference data and the historical benchmark scenario classification strategy.
  • the data division module 504 is configured to divide the historical reference data into different scene categories according to the current scene classification strategy.
  • the second calibration module 505 is configured to use the reference data of each scene category to calibrate the scene-related parameters in the initial BES model to obtain candidate BES models corresponding to the scene category.
  • the evaluation module 506 is used to comprehensively evaluate the scene classification strategy according to any one or any combination of the number of scene categories, the degree of discrimination between scene categories, and the simulation accuracy of the candidate BES models corresponding to each scene category .
  • the result determination module 507 is used to determine the scene classification strategy as a benchmark scene classification strategy and provide it to the scene decision module 412 when the comprehensive evaluation result meets the set requirements, and determine the candidate BES model as a benchmark BES model, And provide it to the BES model determination module 413; when the comprehensive evaluation result does not meet the set requirements, instruct the strategy optimization module 508 to perform strategy optimization.
  • the strategy optimization module 508 is configured to optimize the scenario classification strategy using a set strategy optimization algorithm, use the optimized scenario classification strategy as the current scenario classification strategy, and instruct the data division module 504 to execute the
  • the scene classification strategy is an operation of classifying the historical reference data into different scene categories.
  • the evaluation module 506 and the policy optimization module 508 may not be included, and the result judging module 507 directly determines the scene classification strategy as a benchmark scene classification strategy and provides For the scene decision module 412 , determine the candidate BES model as a reference BES model, and provide it to the BES model determination module 413 .
  • system shown in FIG. 5 may also be incorporated into the system shown in FIG. 4 .
  • Fig. 6 is a schematic structural diagram of another HVAC system management system in the embodiment of the present application, which can be used to implement the method shown in Fig. 1-Fig. 3, or realize the system shown in Fig. 4-Fig. 5 .
  • the system may include: at least one memory 61 and at least one processor 62 .
  • some other components may also be included, such as communication ports and the like. These components communicate via bus 63 .
  • At least one memory 61 is used to store computer programs.
  • the computer program can be understood as including various modules of the management system of the HVAC system shown in FIGS. 4-5 .
  • at least one memory 61 can also store an operating system and the like.
  • the operating system includes but is not limited to: Android operating system, Symbian operating system, Windows operating system, Linux operating system and so on.
  • At least one processor 62 is used to invoke a computer program stored in at least one memory 61 to execute the management method of the HVAC system described in the embodiment of the present application.
  • the processor 62 may be a CPU, a processing unit/module, an ASIC, a logic module or a programmable gate array, and the like. It can receive and send data through the communication port.
  • At least one processor 62 is used to call at least one computer program stored in the memory 61 to make the system perform corresponding operations.
  • the operation may include: determining the current scene category according to the acquired current scene data related to the operation of the HVAC system and a predetermined benchmark scene classification strategy;
  • the benchmark BES model of the current scene category is obtained to obtain the current benchmark BES model;
  • the optimal control strategy of the HVAC system is determined by using the current scene data and the current benchmark BES model; and the optimal control strategy is controlled according to the optimal control strategy Describe the corresponding equipment in the HVAC system.
  • the operations may further include: collecting a set number of historically collected historical reference data related to the HVAC system; the set number is greater than a set number threshold; using the historical reference The data calibrates the parameters irrelevant to the scene in an initial BES model; determine the current scene classification strategy according to the historical reference data; divide the historical reference data into different scene categories according to the current scene classification strategy, Utilize the reference data of each scene category to calibrate the parameters relevant to the scene in the initial BES model, obtain the candidate BES model corresponding to the scene category; determine the scene classification strategy as the benchmark scene classification strategy, and The candidate BES model is determined to be the reference BES model.
  • before determining the scene classification strategy as the benchmark scene classification strategy and determining the candidate BES model as the benchmark BES model further include: according to the number of scene categories, the relationship between scene categories Any one or any combination of the degree of discrimination and the simulation accuracy of the candidate BES models corresponding to each scene category, comprehensively evaluate the scene classification strategy; when the comprehensive evaluation results meet the set requirements, perform the scene classification The classification strategy is determined as the benchmark scene classification strategy, and the candidate BES model is determined as the operation of the benchmark BES model; otherwise, when the comprehensive evaluation result does not meet the set requirements, the set strategy optimization algorithm is used to The scene classification strategy is optimized, and the optimized scene classification strategy is used as the current scene classification strategy, and the operation of dividing the historical reference data into different scene categories according to the current scene classification strategy is returned.
  • the operation before determining the current scene classification strategy according to the historical reference data, the operation further includes: determining whether there is a historical benchmark scene classification strategy, and if there is a historical benchmark scene classification strategy, then according to The historical reference data and the historical benchmark scene classification strategy determine the current scene classification strategy, and if there is no historical benchmark scene classification strategy, the operation of determining the current scene classification strategy according to the historical reference data is performed.
  • the operations further include: displaying information related to the HVAC system; the information related to the HVAC system comes from the current scene data, and/or the control of the HVAC system information and/or status information.
  • the scene data includes at least one or any combination of the following information: weather information, holiday information, local event information, sensor collection related to the HVAC system and/or building thermal performance in the building information, HVAC system-related information manually entered in the building.
  • the historical reference data includes historical scene data.
  • a hardware module may include specially designed permanent circuits or logic devices (such as special-purpose processors, such as FPGAs or ASICs) to perform specific operations.
  • Hardware modules may also include programmable logic devices or circuits (eg, including general-purpose processors or other programmable processors) temporarily configured by software to perform particular operations.
  • programmable logic devices or circuits eg, including general-purpose processors or other programmable processors
  • an embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, the computer program can be executed by a processor and realize the management of the HVAC system described in the embodiment of the present application method.
  • a system or device equipped with a storage medium may be provided, on which the software program code for realizing the functions of any implementation manner in the above-mentioned embodiments is stored, and the computer (or CPU or MPU of the system or device) ) to read and execute the program code stored in the storage medium.
  • some or all of the actual operations can also be completed by an operating system or the like operating on the computer through instructions based on program codes.
  • Embodiments of storage media for providing program codes include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), Tape, non-volatile memory card, and ROM.
  • the program code can be downloaded from a server computer via a communication network.
  • multiple scene categories of buildings are determined according to a large amount of historical reference data collected historically, and a corresponding BES model is set corresponding to each scene category.
  • the current scene category can be determined according to the currently collected scene data, and the BES model corresponding to the current scene category can be called to determine the optimal control strategy of the HVAC system, and then the HVAC system can be controlled based on the optimal control strategy
  • the corresponding equipment in the building such as heating equipment, ventilation equipment and air conditioning equipment, etc., thereby improving the accuracy of the BES model in energy performance prediction, which is conducive to the optimization of building energy conservation.

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  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Marketing (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Air Conditioning Control Device (AREA)

Abstract

L'invention concerne un procédé et un système de gestion d'un système de chauffage, de ventilation et de climatisation, et un support de stockage. Le procédé comprend : la détermination d'une catégorie de scénario en cours en fonction de données de scénario en cours obtenues relatives au fonctionnement du système de chauffage, de ventilation et de climatisation et d'une stratégie de classification de scénario de référence prédéterminée ; l'activation, à partir d'un modèle de simulation d'énergie de bâtiment (BES) de référence prédéterminée correspondant à chaque catégorie de scénario, d'un modèle BES de référence correspondant à la catégorie de scénario en cours pour obtenir un modèle BES de référence en cours ; la détermination d'une stratégie de commande optimale du système de chauffage, de ventilation et de climatisation à l'aide des données de scénario en cours et du modèle BES de référence en cours ; et la commande d'un dispositif correspondant dans le système de chauffage, de ventilation et de climatisation selon la stratégie de commande optimale. Le procédé peut améliorer la précision du modèle BES dans une prédiction de performance énergétique, et est avantageux pour construire une optimisation d'économie d'énergie.
PCT/CN2021/101031 2021-06-18 2021-06-18 Procédé et système de gestion de chauffage, de ventilation et de climatisation, et support de stockage WO2022261965A1 (fr)

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PCT/CN2021/101031 WO2022261965A1 (fr) 2021-06-18 2021-06-18 Procédé et système de gestion de chauffage, de ventilation et de climatisation, et support de stockage

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