CN117121025A - Management method, system and storage medium of heating ventilation air conditioning system - Google Patents
Management method, system and storage medium of heating ventilation air conditioning system Download PDFInfo
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Abstract
A management method, system and storage medium for heating ventilation air conditioning system. The method comprises the following steps: determining a current scene category according to the acquired current scene data related to the operation of the heating ventilation air conditioning system and a predetermined reference scene classification strategy; activating a reference BES model corresponding to the current scene category from the predetermined building energy consumption simulation reference BES models corresponding to the scene categories to obtain a current reference BES model; determining an optimal control strategy of the heating ventilation air conditioning system by utilizing the current scene data and the current reference BES model; and controlling corresponding equipment in the heating, ventilation and air conditioning system according to the optimal control strategy. The method can improve the accuracy of the BES model in energy performance prediction, and is beneficial to building energy conservation optimization.
Description
The invention relates to the field of building energy consumption, in particular to a management system, a management method and a computer readable storage medium of a heating ventilation air conditioning system.
Reducing building energy consumption is one of the primary tasks of many national official energy policies, and a significant portion of the building energy consumption of industrialized countries is used in Heating, ventilation and air conditioning (HVAC) systems, hereinafter referred to as HVAC systems. The implementation of optimized operating parameters, such as chilled water temperature and supply air temperature, in a hvac system can reduce building energy consumption without sacrificing thermal comfort or significant modification of the building/system.
However, due to the high variability of the design and control of the building, hvac system and the external environment, conventional arrangements may not necessarily save energy during daily operation. Building energy consumption digital twin systems based on building energy consumption simulation (Building Energy Simulation, BES) are an energy-saving optimization method applied in recent years to improve the energy efficiency of existing buildings.
Disclosure of Invention
In view of this, in the embodiments of the present invention, a management method of a hvac system is provided on one hand, and a management system and a computer readable storage medium of a hvac system are provided on the other hand, so as to improve accuracy of a BES model in energy performance prediction, which is beneficial to energy saving optimization of a building.
The embodiment of the invention provides a heating ventilation air conditioning management method, which comprises the following steps: determining a current scene category according to the acquired current scene data related to the operation of the heating ventilation air conditioning system and a predetermined reference scene classification strategy; activating a reference BES model corresponding to the current scene category from the predetermined building energy consumption simulation reference BES models corresponding to the scene categories to obtain a current reference BES model; determining an optimal control strategy of the heating ventilation air conditioning system by utilizing the current scene data and the current reference BES model; and controlling corresponding equipment in the heating, ventilation and air conditioning system according to the optimal control strategy.
In one embodiment, the method further comprises: collecting historical reference data which are collected by a set number of groups and are related to a heating ventilation air conditioning system; the set number is greater than a set number threshold; calibrating scene-independent parameters in an initial BES model by using the historical reference data; determining a current scene classification strategy according to the historical reference data; dividing the historical reference data into different scene categories according to the current scene classification strategy, and calibrating parameters related to the scene in the initial BES model by using the reference data of each scene category to obtain candidate BES models corresponding to the scene categories; and determining the scene classification strategy as the reference scene classification strategy, and determining the candidate BES model as the reference BES model.
In one embodiment, before determining the scene classification strategy as the reference scene classification strategy and determining the candidate BES model as the reference BES model, further comprising: comprehensively evaluating the scene classification strategy according to any one or any combination of the number of scene categories, the distinction between the scene categories and the simulation accuracy of the candidate BES model corresponding to each scene category; when the comprehensive evaluation result meets the set requirement, executing the operation of determining the scene classification strategy as the reference scene classification strategy and determining the candidate BES model as the reference BES model; otherwise, when the comprehensive evaluation result does not meet the set requirement, the set strategy optimization algorithm is utilized to optimize the scene classification strategy, 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 carried out in a return mode.
In one embodiment, the initial BES model is a BES model established using known fixed information related to operation of the HVAC system and initial estimates of uncertainty information, or is a currently available BES model.
In one embodiment, before the determining the current scene classification policy according to the historical reference data, the method further includes: determining whether a historical reference scene classification strategy exists currently, if so, determining the current scene classification strategy according to the historical reference data and the historical reference scene classification strategy, and if not, executing the operation of determining the current scene classification strategy according to the historical reference data.
In one embodiment, further comprising: displaying information related to the hvac system; the information related to the hvac system is derived from the current scene data and/or control information and/or status information of the hvac system.
In one embodiment, the scene data includes at least one or any combination of the following information: weather information, holiday information, local event information, information collected by sensors related to heating, ventilation and air conditioning systems and/or building thermal properties in a building, and information related to heating, ventilation and air conditioning systems manually input in a building; the historical reference data includes historical scene data.
The management system of the heating ventilation air conditioning system provided by the embodiment of the invention comprises: at least one memory and at least one processor, wherein: the at least one memory is used for storing a computer program; the at least one processor is configured to invoke a computer program stored in the at least one memory to cause the apparatus to perform corresponding operations comprising: determining a current scene category according to the acquired current scene data related to the operation of the heating ventilation air conditioning system and a predetermined reference scene classification strategy; activating a reference BES model corresponding to the current scene category from the predetermined building energy consumption simulation reference BES models corresponding to the scene categories to obtain a current reference BES model; determining an optimal control strategy of the heating ventilation air conditioning system by utilizing the current scene data and the current reference BES model; and controlling corresponding equipment in the heating, ventilation and air conditioning system according to the optimal control strategy.
In one embodiment, the operations further comprise: collecting historical reference data which are collected by a set number of groups and are related to a heating ventilation air conditioning system; the set number is greater than a set number threshold; calibrating scene-independent parameters in an initial BES model by using the historical reference data; determining a current scene classification strategy according to the historical reference data; dividing the historical reference data into different scene categories according to the current scene classification strategy, and calibrating parameters related to the scene in the initial BES model by using the reference data of each scene category to obtain candidate BES models corresponding to the scene categories; and determining the scene classification strategy as the reference scene classification strategy, and determining the candidate BES model as the reference BES model.
In one embodiment, determining the scene classification strategy as the reference scene classification strategy, before determining the candidate BES model as the reference BES model, further comprises: comprehensively evaluating the scene classification strategy according to any one or any combination of the number of scene categories, the distinction between the scene categories and the simulation accuracy of the candidate BES model corresponding to each scene category; when the comprehensive evaluation result meets the set requirement, executing the operation of determining the scene classification strategy as the reference scene classification strategy and determining the candidate BES model as the reference BES model; otherwise, when the comprehensive evaluation result does not meet the set requirement, the set strategy optimization algorithm is utilized to optimize the scene classification strategy, 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 carried out in a return mode.
In one embodiment, before the determining the current scene classification policy according to the historical reference data, the operations further comprise: determining whether a historical reference scene classification strategy exists currently, if so, determining the current scene classification strategy according to the historical reference data and the historical reference scene classification strategy, and if not, executing the operation of determining the current scene classification strategy according to the historical reference data.
In one embodiment, the operations further comprise: displaying information related to the hvac system; the information related to the hvac system is derived from the current scene data and/or control information and/or status information of the hvac system.
The management system of the heating ventilation air conditioning system provided in the embodiment of the invention comprises: the data collection module is used for collecting current scene data related to the operation of the heating ventilation air conditioning system; the scene decision module is used for determining the current scene category of the building according to the current scene data and a predetermined reference scene classification strategy; the model determining module is used for activating a reference BES model corresponding to the current scene category from the predetermined reference building energy consumption simulation BES models corresponding to the scene categories to obtain a current reference BES model; the control strategy determining module is used for calling the current reference BES model based on a set optimal control algorithm and the current scene data, and determining an optimal control strategy of the heating ventilation air conditioning system according to the output of the current reference BES model; and the system control module is used for controlling corresponding equipment in the heating, ventilation and air conditioning system according to the optimal control strategy.
In one embodiment, further comprising: the data recording module is used for recording historical reference data which are collected by the data collecting module and/or other external modules in a historical manner and are related to the heating ventilation air conditioning system, wherein the set number of the historical reference data are collected by the data collecting module and/or other external modules; the set number is greater than a set number threshold; the first calibration module is used for calibrating parameters irrelevant to a scene in an initial BES model by utilizing the historical reference data; the classification strategy determining module is used for determining a current scene classification strategy according to the historical reference data; the data dividing module is used for dividing the historical reference data into different scene categories according to the current scene classification strategy; the second calibration module is used for calibrating parameters related to the scene in the initial BES model by utilizing the reference data of each scene category to obtain candidate BES models corresponding to the scene categories; the evaluation module is used for comprehensively evaluating the scene classification strategy according to any one or any combination of the number of scene categories, the distinction between the scene categories and the simulation accuracy of the candidate BES model corresponding to each scene category; the result judging module is used for determining the scene classification strategy as a reference scene classification strategy and providing the reference scene classification strategy to the scene decision module when the comprehensive evaluation result meets the set requirement, determining the candidate BES model as a reference BES model and providing the reference BES model to the BES model determining module; when the comprehensive evaluation result does not meet the set requirement, the strategy optimization module is instructed to perform strategy optimization; and the policy optimization module is used for optimizing the scene classification policy by using a set policy optimization algorithm, taking the optimized scene classification policy as a current scene classification policy, and indicating the data partitioning module to execute the operation of partitioning the historical reference data into different scene categories according to the current scene classification policy.
A computer-readable storage medium according to an embodiment of the present invention has a computer program stored thereon; the computer program can be executed by a processor and implement the method for managing a hvac system according to any one of the embodiments.
As can be seen from the above solution, in the embodiment of the present invention, a plurality of scene categories of a building are determined according to a large amount of history reference data collected by histories, and a corresponding BES model is set corresponding to each scene category. And then, determining the current scene type according to the currently acquired scene data, calling the BES model corresponding to the current scene type to determine the optimal control strategy of the heating ventilation air conditioning system, and controlling corresponding equipment in the heating ventilation air conditioning system, such as heating air equipment, ventilation equipment, air conditioning equipment and the like based on the optimal control strategy, thereby improving the accuracy of the BES model in energy performance prediction and being beneficial to building energy conservation optimization.
The above and other features and advantages of the present invention will become more apparent to those of ordinary skill in the art by describing in detail preferred embodiments thereof with reference to the attached drawings in which:
fig. 1 is an exemplary flowchart of a management method of a hvac system according to an embodiment of the present invention.
FIG. 2 is an exemplary flow chart of a method of establishing and updating a reference scene classification strategy and reference BES models corresponding to respective scene categories in one example of the application.
FIG. 3 is a schematic diagram of a process for classifying historical reference data, calibrating an initial BES model, and obtaining candidate BES models for each scene category in accordance with one example of the present application.
Fig. 4 is a schematic structural diagram of a management system of a hvac system according to an embodiment of the present application.
FIG. 5 is a schematic diagram of a system for creating and updating a reference scene classification strategy and a reference BES model corresponding to each scene class in an embodiment of the present application.
Fig. 6 is a schematic structural diagram of a management system of a hvac system according to another embodiment of the present application.
Wherein, the reference numerals are as follows:
| reference numerals | Meaning of |
| 101～104；201～208 | Step (a) |
| 31 | Reference data |
| 32 | Scene classification strategy |
| 331、332、……、33n | Data subsets under different scene categories |
| 34 | Model calibration |
| 351、352、……、35n | Candidate BES models under different scene categories |
| 41 | Management system of heating ventilation air conditioning system |
| 411 | Data collection module |
| 412 | Scene decision module |
| 413 | Model determination module |
| 4131、4132、……、413n | Benchmark BES models under different scene categories |
| 414 | Control strategy determination module |
| 415 | System control module |
| 42 | Building construction |
| 421 | Sensor for detecting a position of a body |
| 422 | Control panel |
| 423 | Heating ventilation air conditioning system |
| 43 | External data source |
| 501 | Data recording module |
| 502 | First calibration module |
| 503 | Classification policy determination module |
| 504 | Data dividing module |
| 505 | Second calibration module |
| 506 | Evaluation module |
| 507 | Result determination module |
| 508 | Policy optimization module |
| 61 | Memory device |
| 62 | Processor and method for controlling the same |
| 63 | Bus line |
In the embodiment of the invention, considering that the energy consumption of a heating ventilation air conditioning system is optimized at present, and meanwhile, the specified building performance standard is maintained, building energy consumption simulation (Building Energy Simulation, BES) is generally applied in the building operation stage to predict the influence of heating ventilation air conditioning operation parameters under the influence of certain external environment.
BES models can be generally classified into rule-driven models and data-driven models. The data-driven BES model uses the monitored data in the building to generate a model that predicts system behavior, the accuracy of which is largely dependent on the quality and quantity of available training data. Rule-driven models based on thermal equilibrium equations and data-driven models can provide accurate assessment of the most detailed predictions of building performance and control strategies. Since hundreds or thousands of input parameters are used in a rule-driven BES model, some of which are typically not measurable, a significant difference between the predicted building energy consumption of the BES model and the actual metered building energy consumption can be found. Therefore, calibration and testing of BES models using monitoring data during operation of the building is required. Considering the impact of the external environment on the performance of hvac systems, operators typically update the BES model at a fixed frequency, for example, every three months, according to empirical rules.
However, in the current BES model calibration process, due to the influence of external environment, the change of model parameters cannot be completely reflected, for example, the ventilation rate of a building has a great relationship with the air pollution index and the outdoor temperature. Therefore, in this embodiment, it is considered that a plurality of scene categories of the building are determined according to a large amount of external information (such as weather information, holiday information, local event information, etc.) and building status information (such as information collected by sensors, manual input information in the building, etc.), and a corresponding BES model is set for each scene category. And then, determining the current scene type according to the external information and the building state information which are collected currently, calling a BES model corresponding to the current scene type to determine an optimal control strategy of the heating ventilation air conditioning system, and controlling corresponding equipment in the heating ventilation air conditioning system, such as heating fan equipment, ventilation equipment, air conditioning equipment and the like, based on the optimal control strategy.
The present invention will be further described in detail with reference to the following examples, in order to make the objects, technical solutions and advantages of the present invention more apparent.
Fig. 1 is an exemplary flowchart of a management method of a hvac system according to an embodiment of the present invention. As shown in fig. 1, the method in this embodiment may include the following steps:
Step 101, determining a current scene category according to the acquired current scene data related to the operation of the heating ventilation air conditioning system and a predetermined reference scene classification strategy.
In this embodiment, 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, etc. Wherein building status data from within the building may include: information collected by sensors associated with heating ventilation and air conditioning systems and/or building thermal performance within the building; and manually entered information related to the hvac system within the building. Wherein, the information collected by the sensor can comprise: the temperature of chilled water/hot water, the power usage of the chiller/heater, the flow rate of a Fan Control Unit (FCU) terminal, the indoor temperature and humidity, etc. 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, and the like. In practical applications, the specific scene data may be determined according to practical needs, which is not limited herein.
In this embodiment, in a specific implementation, a large number of sets of historical scene data may be used as input samples based on a predetermined reference scene classification policy, a scene category corresponding to each set of historical scene data is used as an output sample, an artificial intelligent network is trained, and a scene classification model is obtained, and then current scene data may be used as an input of the scene classification model, where the scene classification model may directly output the corresponding scene category.
In addition, a scene database may be set based on a predetermined reference scene classification policy, where scene data for each scene category may be stored, and after receiving current scene data, various artificial intelligence algorithms, such as K-means Clustering (KMeans) or Density-based noise spatial Clustering (Density-Based Spatial Clustering Algorithm with Noise, DBSCAN) may be used to determine the scene category corresponding to the current scene data from the scene database.
Step 102, activating a reference BES model corresponding to the current scene category from the predetermined reference BES models corresponding to the scene categories to obtain a current reference BES model.
In this embodiment, instead of setting only a single BES model for a building, a reference BES model is set for each of the different scene categories. In this way, after determining the current scene category in step 101, the reference BES model corresponding to the scene category can be activated as a simulation model of building performance and energy consumption, so that the simulation result of the reference BES model can be more accurate.
And step 103, determining an optimal control strategy of the heating ventilation air conditioning system by using the current scene data and the current reference BES model.
In this step, an optimal control strategy of the hvac system may be determined based on an optimal control algorithm, such as a Genetic Algorithm (GA) or a particle swarm algorithm (PSO) or a combination algorithm of GA and PSO, so as to ensure performance indexes of the building in the current scenario and have optimal energy-saving performance. Specifically, the current BES model may be called based on a set optimization control algorithm and the current scene data, for example, the current scene data and other different input parameters obtained based on the optimization control algorithm may be used as the input of the current reference BES model, different outputs of the current reference BES model are obtained, and an optimal control strategy of the heating ventilation and air conditioning system is determined according to the outputs of the current reference BES model.
In one example, the operating parameters in the optimal control strategy that need to be optimized may be chilled/hot water temperature, air flow per FCU, etc.
And 104, controlling corresponding equipment in the heating, ventilation and air conditioning system according to the optimal control strategy.
In this step, corresponding control signals can be sent to the warm air, ventilation and air conditioning system equipment in the building according to the optimal control strategy.
In addition, information related to the hvac system may be further displayed in this embodiment. The information related to the hvac system is derived from the current scene data and/or control information and/or status information of the hvac system. For example, information related to the building thermal and hvac system such as indoor temperature, energy consumption, hvac equipment status, etc. may be displayed.
The above method may be periodically performed according to a predetermined time interval. Alternatively, in some applications, it may be performed in real time.
There are various specific implementation methods for the above-mentioned reference scene classification policies in step 101 and step 102 and the process of establishing and updating the reference BES model corresponding to each scene category. One of them is listed below.
FIG. 2 is an exemplary flow chart of a method for building and updating a reference scene classification strategy and a reference BES model corresponding to each scene class in one example of the invention. In this example, the update process may be performed periodically according to a predetermined time interval. As shown in fig. 2, the method may include the steps of:
step 201, collecting historical reference data which is collected by a set number of groups and is related to a heating ventilation air conditioning system; the set number is greater than a set number threshold. The set quantity threshold is a value large enough to meet the requirement.
In this step, the historical reference data may include historical scene data and other related data, such as control strategies, energy consumption, and the like. And in particular, may be determined according to actual needs, and is not limited herein.
In practical application, along with accumulation of historical reference data, when the reference scene classification strategy and the reference BES model corresponding to each scene category are updated later, the amount of the historical reference data is increased. Correspondingly, the updated reference scene classification strategy and the reference BES model corresponding to each scene category are also more and more optimized.
Step 202, calibrating scene-independent parameters in an initial BES model 20 using the historical reference data.
In this step, the initial BES model may be a BES model established using known fixed information related to operation of the HVAC system, such as geometry information, structural information, HVAC system information, etc., and initial estimates of uncertainty information, such as HVAC plant performance, occupancy, etc. Or if no major repairs are made to the building and hvac system, the initial BES model may be an already existing currently available BES model.
In this step, the scene-independent parameter may be some structural feature data or the like.
Step 203, when the reference scene classification policy and the reference BES model corresponding to each scene category are initially established, the current scene classification policy may be determined according to the historical reference data in this step. When the reference scene classification strategy and the reference BES model corresponding to each scene category are updated subsequently, the current scene classification strategy can be determined directly according to the historical reference data, and the previous reference scene classification strategy can be referred to, namely when the historical reference scene classification strategy exists, the current scene classification strategy can be determined according to the historical reference data and the historical reference scene classification strategy.
In this step, the scene classification policy may be determined according to practical situations, which is not limited herein. For example, the classification criteria may be maximum temperature, minimum temperature, average temperature, precipitation, air pollution index, wind speed, weekday/weekend, other parameters related to weather and/or building operation, and/or combinations of these parameters.
And 204, dividing the historical reference data into different scene categories according to the current scene classification strategy, and calibrating parameters related to the scene in the initial BES model by using the reference data of each scene category to obtain candidate BES models corresponding to the scene categories.
The process in this step can be seen in fig. 3, where fig. 3 shows a schematic diagram of the process of classifying historical reference data, calibrating the initial BES model, and obtaining candidate BES models for each scene category. As shown in fig. 3, the process may include:
the reference data 31 is divided into different scene categories based on the scene classification policy 32, resulting in a data subset 331 under scene category 1, data subsets 332, … … under scene category 2, and data subset 33n under scene category n. And carrying out model calibration 34 on the initial BES model by utilizing the data subset under each scene category to obtain a candidate BES model 351 under the scene category 1, candidate BES models 352 and … … under the scene category 2 and a candidate BES model 35n under the scene category n.
If it can be determined that the current scene classification policy and the candidate BES model can meet the requirements, for example, the current scene classification policy and the candidate BES model are the scene classification policy and the candidate BES model obtained after multiple updates, the scene classification policy may be directly determined as the reference scene classification policy, and the candidate BES model may be determined as the reference BES model.
Of course, if it cannot be determined whether the current scene classification strategy and the candidate BES model meet the requirements, the following steps may be continued as shown in fig. 2:
step 205, comprehensively evaluating the scene classification strategy according to any one or any combination of the number of scene categories, the distinction between scene categories and the simulation accuracy of the candidate BES model corresponding to each scene category.
In this step, when calculating the simulation accuracy of the candidate BES model corresponding to each scene category, an error, such as an average error or a root mean square error, between the simulation result of the candidate BES model and the result of the history sample may be calculated for each candidate BES model, thereby obtaining an evaluation score, and then the evaluation score of the candidate BES model corresponding to each scene category is subjected to a comprehensive process, such as weighted summation, thereby obtaining a comprehensive score, and then the comprehensive score may be compared with a threshold value, so as to determine whether the simulation accuracy of the candidate BES model corresponding to each scene category meets the requirement.
Step 206, judging whether the evaluation result meets the requirement, and executing step 207 when the comprehensive evaluation result meets the set requirement; otherwise, step 208 is performed.
Step 207, determining the scene classification strategy as the reference scene classification strategy, and determining the candidate BES model as the reference BES model.
And step 208, optimizing the scene classification strategy by using a set strategy optimization algorithm, taking the optimized scene classification strategy as the current scene classification strategy, and returning to the execution step 204.
In this step, an artificial intelligence algorithm such as a Genetic Algorithm (GA) or a particle swarm algorithm (PSO) or a combination algorithm of GA and PSO, etc. may be used to optimize the scene classification strategy. In addition, a simulation acceleration algorithm, such as a proxy model, may also be employed to generate an optimized scene classification strategy.
The above describes the management method of the hvac system in the embodiment of the present invention in detail, and the following describes the management system of the hvac system in the embodiment of the present invention in detail. The management system of the hvac system in the embodiment of the present invention may be used to implement the management method of the hvac system in the embodiment of the present invention, and details not disclosed in detail in the embodiment of the system of the present invention may be referred to corresponding descriptions in the embodiment of the method of the present invention, and will not be described in detail here.
Fig. 4 is an exemplary block diagram of a management system of a hvac system according to an embodiment of the present invention. As shown in fig. 4, the management system 41 of the hvac system may include: a data collection module 411, a scene 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 configured to collect current scene data related to operation of the hvac system, process the collected data, and send the processed data to the digital interface of the scene decision module 412. As shown in fig. 4, the data collection module 411 may collect information collected by sensors 421 within the building 42 relating to the heating ventilation and air conditioning system and/or the thermal performance of the building; information manually entered through control panel 422 and the like within building 42 may also be collected; information from external data sources 43 may also be collected, such as weather information, holiday information, local event information, and the like.
The scene decision module 412 is configured to determine a current scene category of the building according to the current scene data and a predetermined reference scene classification policy.
The model determining module 413 is configured to activate a reference BES model corresponding to the current scene category from predetermined reference BES models 4131, 4132, … …, 413n corresponding to each scene category, to obtain a current reference BES model.
Wherein the scene decision module 412, the model determination module 413, and the BES model corresponding to each scene category may be components in the digital twin system DT of the hvac system.
The control strategy determining module 414 is configured to invoke the current BES model based on a set optimization control algorithm and the current scene data, and determine an optimal control strategy of the hvac system according to an output of the current reference BES model.
The system control module 415 is configured to control corresponding devices in the hvac system 423 in the building 42, such as a warm air device, a ventilation device, and an air conditioning device, according to the optimal control strategy.
In addition, the management system of the hvac system according to the embodiment of the present application may further include a display module 416, as shown in the dashed line portion in fig. 4, where the display module 416 is configured to read information related to the building thermal and hvac system from at least one of the data collection module 411, the current reference BES model, and the system control module 415, and display the information.
FIG. 5 is a schematic diagram of a system for creating and updating a reference scene classification strategy and a reference BES model corresponding to each scene class in an embodiment of the present application. As shown in fig. 5, the system may include: a data logging module 501, a first calibration module 502, a classification policy determination module 503, a data partitioning module 504, a second calibration module 505, an evaluation module 506, a result determination module 507, and a policy optimization module 508.
The data recording module 501 is configured to record a set number of sets of historical reference data related to the hvac system collected by the data collecting module and/or other external modules; the set number is greater than a set number threshold.
The first calibration module 502 is configured to calibrate scene-independent parameters in an initial BES model using the historical reference data.
The classification policy determining module 503 is configured to determine a current scene classification policy according to the historical reference data. The classification policy determination module 503 may further refer to the historical reference scene classification policy when the historical reference scene classification policy exists, for example, may determine the current scene classification policy according to the historical reference data and the historical reference scene classification policy.
The data dividing module 504 is configured to divide the historical reference data into different scene categories according to the current scene classification policy.
The second calibration module 505 is configured to calibrate parameters related to a scene in the initial BES model by using reference data of each scene category, so as to obtain candidate BES models corresponding to the scene category.
The evaluation module 506 is configured to comprehensively evaluate the scene classification policy according to any one or any combination of the number of scene categories, the distinction between scene categories, and the simulation accuracy of the candidate BES models corresponding to each scene category.
The result determination module 507 is configured to determine the scene classification policy as a reference scene classification policy and provide the reference scene classification policy to the scene decision module 412, determine the candidate BES model as a reference BES model and provide the reference BES model to the BES model determination module 413 when the comprehensive evaluation result meets a set requirement; and when the comprehensive evaluation result does not meet the set requirement, the strategy optimization module 508 is instructed to perform strategy optimization.
The policy optimization module 508 is configured to optimize the scene classification policy by using a set policy optimization algorithm, take the optimized scene classification policy as a current scene classification policy, and instruct the data partitioning module 504 to execute the operation of partitioning the historical reference data into different scene categories according to the current scene classification policy.
Corresponding to the method shown in fig. 2, in some applications, the evaluation module 506 and the policy optimization module 508 may not be included, and the result determination module 507 may directly determine the scene classification policy as a reference scene classification policy and provide the reference scene classification policy to the scene decision module 412, and determine the candidate BES model as a reference BES model and provide the reference BES model to the BES model determination module 413.
In particular, the system of fig. 5 may also be incorporated into the system of fig. 4.
Fig. 6 is a schematic structural diagram of a management system of a heating ventilation and air conditioning system according to another embodiment of the present application, which may be used to implement the methods shown in fig. 1 to 3 or implement the systems shown in fig. 4 to 5. As shown in fig. 6, the system may include: at least one memory 61 and at least one processor 62. In addition, some other components may be included, such as communication ports and the like. These components communicate via a bus 63.
Wherein the at least one memory 61 is used for storing a computer program. In one embodiment, the computer program may be understood to include the various modules of the management system of the hvac system shown in fig. 4-5. In addition, the at least one memory 61 may also store an operating system or the like. Operating systems include, but are not limited to: android operating system, symbian operating system, windows operating system, linux operating system, etc.
The at least one processor 62 is configured to invoke the computer program stored in the at least one memory 61 to execute the management method of the hvac system according to the embodiment of the present application. The processor 62 may be a CPU, processing unit/module, ASIC, logic module, or programmable gate array, among others. Which can receive and transmit data through the communication port.
In particular, the at least one processor 62 is adapted to invoke the computer program stored in the at least one memory 61 to cause the system to perform the corresponding operations. The operations may include: determining a current scene category according to the acquired current scene data related to the operation of the heating ventilation air conditioning system and a predetermined reference scene classification strategy; activating a reference BES model corresponding to the current scene category from the predetermined reference BES models corresponding to the scene categories to obtain a current reference BES model; determining an optimal control strategy of the heating ventilation air conditioning system by utilizing the current scene data and the current reference BES model; and controlling corresponding equipment in the heating, ventilation and air conditioning system according to the optimal control strategy.
In one embodiment, the operations may further comprise: collecting historical reference data which are collected by a set number of groups and are related to a heating ventilation air conditioning system; the set number is greater than a set number threshold; calibrating scene-independent parameters in an initial BES model by using the historical reference data; determining a current scene classification strategy according to the historical reference data; dividing the historical reference data into different scene categories according to the current scene classification strategy, and calibrating parameters related to the scene in the initial BES model by using the reference data of each scene category to obtain candidate BES models corresponding to the scene categories; and determining the scene classification strategy as the reference scene classification strategy, and determining the candidate BES model as the reference BES model.
In one embodiment, determining the scene classification strategy as the reference scene classification strategy, before determining the candidate BES model as the reference BES model, further comprises: comprehensively evaluating the scene classification strategy according to any one or any combination of the number of scene categories, the distinction between the scene categories and the simulation accuracy of the candidate BES model corresponding to each scene category; when the comprehensive evaluation result meets the set requirement, executing the operation of determining the scene classification strategy as the reference scene classification strategy and determining the candidate BES model as the reference BES model; otherwise, when the comprehensive evaluation result does not meet the set requirement, the set strategy optimization algorithm is utilized to optimize the scene classification strategy, 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 carried out in a return mode.
In one embodiment, before the determining the current scene classification policy according to the historical reference data, the operations further comprise: determining whether a historical reference scene classification strategy exists currently, if so, determining the current scene classification strategy according to the historical reference data and the historical reference scene classification strategy, and if not, executing the operation of determining the current scene classification strategy according to the historical reference data.
In one embodiment, the operations further comprise: displaying information related to the hvac system; the information related to the hvac system is derived from the current scene data and/or control information and/or status information of the hvac system.
In one embodiment, the scene data includes at least one or any combination of the following information: weather information, holiday information, local event information, information collected by sensors associated with heating ventilation and air conditioning systems and/or thermal performance of the building within the building, manually entered information associated with heating ventilation and air conditioning systems within the building. The historical reference data includes historical scene data.
It should be noted that not all the steps and modules in the above processes and the structure diagrams are necessary, and some steps or modules may be omitted according to actual needs. The execution sequence of the steps is not fixed and can be adjusted as required. The division of the modules is merely for convenience of description and the division of functions adopted in the embodiments, and in actual implementation, one module may be implemented by a plurality of modules, and functions of a plurality of modules may be implemented by the same module, and the modules may be located in the same device or different devices.
It will be appreciated that the hardware modules in the embodiments described above may be implemented mechanically or electronically. For example, a hardware module may include specially designed permanent circuits or logic devices (e.g., special purpose processors such as FPGAs or ASICs) for performing certain operations. A hardware module may also include programmable logic devices or circuits (e.g., including a general purpose processor or other programmable processor) temporarily configured by software for performing particular operations. As regards implementation of the hardware modules in a mechanical manner, either by dedicated permanent circuits or by circuits that are temporarily configured (e.g. by software), this may be determined by cost and time considerations.
In addition, the embodiment of the 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 implement the management method of the heating ventilation air conditioning system in the embodiment of the application. Specifically, a system or apparatus provided with a storage medium on which a software program code realizing the functions of any of the above embodiments is stored, and a computer (or CPU or MPU) of the system or apparatus may be caused to read out and execute the program code stored in the storage medium. Further, some or all of the actual operations may be performed by an operating system or the like that is caused to operate on a computer based on instructions of the program code. The program code read out from the storage medium may also be written into a memory provided in an expansion board inserted into a computer or into a memory provided in an expansion unit connected to the computer, and then, based on instructions of the program code, a CPU or the like mounted on the expansion board or the expansion unit may be caused to perform part or all of actual operations, thereby realizing the functions of any of the above embodiments. Storage medium implementations for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD+RWs), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer by a communication network.
As can be seen from the above solution, in the embodiment of the present invention, a plurality of scene categories of a building are determined according to a large amount of history reference data collected by histories, and a corresponding BES model is set corresponding to each scene category. And then, determining the current scene type according to the currently acquired scene data, calling the BES model corresponding to the current scene type to determine the optimal control strategy of the heating ventilation air conditioning system, and controlling corresponding equipment in the heating ventilation air conditioning system, such as heating air equipment, ventilation equipment, air conditioning equipment and the like based on the optimal control strategy, thereby improving the accuracy of the BES model in energy performance prediction and being beneficial to building energy conservation optimization.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (15)
- A method of managing a hvac system, comprising:determining a current scene category (101) according to the acquired current scene data related to the operation of the heating ventilation air conditioning system and a predetermined reference scene classification strategy;Activating a reference BES model corresponding to the current scene category from the predetermined building energy consumption simulation reference BES models corresponding to the scene categories to obtain a current reference BES model (102);determining an optimal control strategy (103) of the heating ventilation air conditioning system by utilizing the current scene data and the current reference BES model;and controlling corresponding equipment (104) in the heating, ventilation and air conditioning system according to the optimal control strategy.
- A method of managing a hvac system according to claim 1, further comprising:collecting historical reference data which are collected by a set number of groups and are related to a heating ventilation air conditioning system; the set number is greater than a set number threshold (201);calibrating scene independent parameters in an initial BES model using the historical reference data (202);determining a current scene classification strategy (203) from the historical reference data;dividing the historical reference data into different scene categories according to the current scene classification strategy, and calibrating parameters related to scenes in the initial BES model by using the reference data of each scene category to obtain candidate BES models (204) corresponding to the scene categories;And determining the scene classification strategy as the reference scene classification strategy, and determining the candidate BES model as the reference BES model.
- The method of claim 2, further comprising, prior to determining the scene classification strategy as the reference scene classification strategy and determining the candidate BES model as the reference BES model:comprehensively evaluating (205) the scene classification strategy according to any one or any combination of the number of scene categories, the distinction between scene categories and the simulation accuracy of the candidate BES models corresponding to each scene category;when the comprehensive evaluation result meets a set requirement, executing the operation (207) of determining the scene classification strategy as the reference scene classification strategy and determining the candidate BES model as the reference BES model; otherwise the first set of parameters is selected,when the comprehensive evaluation result does not meet the set requirement, the set strategy optimization algorithm is utilized to optimize the scene classification strategy, the optimized scene classification strategy is used as a current scene classification strategy (208), and the operation of dividing the historical reference data into different scene categories (204) according to the current scene classification strategy is carried out in a return mode.
- A method of managing a hvac system according to claim 2, wherein the initial BES model is a BES model established using known fixed information related to hvac system operation and initial estimates of uncertainty information, or is a currently available BES model.
- The method of claim 2, further comprising, prior to determining the current scene classification strategy based on the historical reference data:determining whether a historical reference scene classification strategy exists currently, if so, determining the current scene classification strategy according to the historical reference data and the historical reference scene classification strategy, and if not, executing the operation of determining the current scene classification strategy according to the historical reference data.
- The management method of a hvac system according to any one of claims 1 to 5, further comprising: displaying information related to the hvac system; the information related to the hvac system is derived from the current scene data and/or control information and/or status information of the hvac system.
- The method of managing a hvac system according to any one of claims 1 to 5, wherein the scene data includes at least one or any combination of the following information: weather information, holiday information, local event information, information collected by sensors related to heating, ventilation and air conditioning systems and/or building thermal properties in a building, and information related to heating, ventilation and air conditioning systems manually input in a building;the historical reference data includes historical scene data.
- A management system for a hvac system, comprising: at least one memory (61) and at least one processor (62), wherein:the at least one memory (61) is for storing a computer program;the at least one processor (62) is configured to invoke a computer program stored in the at least one memory (61) to cause the apparatus to perform corresponding operations comprising:determining a current scene category according to the acquired current scene data related to the operation of the heating ventilation air conditioning system and a predetermined reference scene classification strategy;activating a reference BES model corresponding to the current scene category from the predetermined building energy consumption simulation reference BES models corresponding to the scene categories to obtain a current reference BES model;Determining an optimal control strategy of the heating ventilation air conditioning system by utilizing the current scene data and the current reference BES model;and controlling corresponding equipment in the heating, ventilation and air conditioning system according to the optimal control strategy.
- The management system of a hvac system of claim 8, wherein the operations further comprise:collecting historical reference data which are collected by a set number of groups and are related to a heating ventilation air conditioning system; the set number is greater than a set number threshold;calibrating scene-independent parameters in an initial BES model by using the historical reference data;determining a current scene classification strategy according to the historical reference data;dividing the historical reference data into different scene categories according to the current scene classification strategy, and calibrating parameters related to the scene in the initial BES model by using the reference data of each scene category to obtain candidate BES models corresponding to the scene categories;and determining the scene classification strategy as the reference scene classification strategy, and determining the candidate BES model as the reference BES model.
- The management system of a hvac system of claim 9, wherein determining the scene classification strategy as the reference scene classification strategy and prior to determining the candidate BES model as the reference BES model, further comprises:Comprehensively evaluating the scene classification strategy according to any one or any combination of the number of scene categories, the distinction between the scene categories and the simulation accuracy of the candidate BES model corresponding to each scene category;when the comprehensive evaluation result meets the set requirement, executing the operation of determining the scene classification strategy as the reference scene classification strategy and determining the candidate BES model as the reference BES model; otherwise the first set of parameters is selected,when the comprehensive evaluation result does not meet the set requirement, the set strategy optimization algorithm is utilized to optimize the scene classification strategy, 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 executed in a returning mode.
- The hvac system management system of claim 8, wherein prior to determining the current scene classification strategy based on the historical reference data, the operations further comprise:determining whether a historical reference scene classification strategy exists currently, if so, determining the current scene classification strategy according to the historical reference data and the historical reference scene classification strategy, and if not, executing the operation of determining the current scene classification strategy according to the historical reference data.
- The management system of a hvac system of any one of claims 8 to 11, wherein the operations further comprise: displaying information related to the hvac system; the information related to the hvac system is derived from the current scene data and/or control information and/or status information of the hvac system.
- A management system for a hvac system, comprising:a data collection module (411) for collecting current scene data related to the operation of the hvac system;a scene decision module (412) configured to determine a current scene category of a building according to the current scene data and a predetermined reference scene classification policy;a model determining module (413) for activating a reference BES model corresponding to the current scene category from the predetermined reference building energy consumption simulation BES models corresponding to the scene categories to obtain a current reference BES model;a control strategy determining module (414) for calling the current reference BES model based on a set optimization control algorithm and the current scene data, and determining an optimal control strategy of the heating ventilation air conditioning system according to the output of the current reference BES model;And the system control module (415) is used for controlling corresponding equipment in the heating, ventilation and air conditioning system according to the optimal control strategy.
- The management system of a hvac system of claim 13, further comprising:a data recording module (501) for recording a set number of sets of historical reference data related to the hvac system collected by the data collection module and/or other external modules; the set number is greater than a set number threshold;a first calibration module (502) for calibrating scene-independent parameters in an initial BES model using the historical reference data;a classification policy determination module (503) configured to determine a current scene classification policy according to the historical reference data;a data partitioning module (504) configured to partition the historical reference data into different scene categories according to the current scene classification policy;a second calibration module (505) configured to calibrate parameters related to a scene in the initial BES model by using reference data of each scene category, so as to obtain candidate BES models corresponding to the scene category;the evaluation module (506) is used for comprehensively evaluating the scene classification strategy according to any one or any combination of the number of scene categories, the distinction between the scene categories and the simulation accuracy of the BES model corresponding to each scene category;A result determination module (507) configured to determine the scene classification policy as a reference scene classification policy and provide the reference scene classification policy to the scene decision module (412), and determine the candidate BES model as a reference BES model and provide the reference BES model to the BES model determination module (413) when the comprehensive evaluation result meets a set requirement; when the comprehensive evaluation result does not meet the set requirement, the strategy optimization module (508) is instructed to perform strategy optimization;the policy optimization module (508) is configured to optimize the scene classification policy by using a set policy optimization algorithm, take the optimized scene classification policy as a current scene classification policy, and instruct the data partitioning module (504) to execute the operation of partitioning the historical reference data into different scene categories according to the current scene classification policy.
- A computer readable storage medium having a computer program stored thereon; the computer program is executable by a processor and implements the method of managing a hvac system according to any one of claims 1 to 7.
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| PCT/CN2021/101031 WO2022261965A1 (en) | 2021-06-18 | 2021-06-18 | Method and system for managing heating, ventilation and air conditioning system, and storage medium |
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