Combined cooling and heating system and method
By integrating meteorological and flow data acquisition modules in the hot and cold supply system, combining building information, and using multi-dimensional prediction of building cold loads, the problem of low accuracy of cooling load prediction in the existing technology is solved, and more efficient energy utilization.
Patent Information
- Application Number
- CN202411352797.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-26
- Publication Date
- 2025-02-18
AI Technical Summary
When predicting cooling loads, the prior art only relies on weather data, resulting in poor prediction accuracy and the multi-dimensional impact of meteorology and people flow cannot be fully considered.
A hot and cold supply system was designed to obtain the current meteorological data and flow data through the meteorological acquisition module and the flow data of the people, and to obtain the building service life with the building information collection module, and to use the cold load prediction module and the data fusion processing module to obtain the building from Multiple dimensions predict building cooling load, and finally the cooling equipment is adjusted through the equipment adjustment module.
By predicting building cooling loads in multiple dimensions, the accuracy of cooling load prediction is significantly improved, the problem of insufficient or excessive cooling loads is avoided, and the energy utilization efficiency is optimized.
Smart Images
Figure CN119476765A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of heating data processing, and in particular to a combined cooling and heating system and method. Background Art
[0002] At present, with the call of environmental friendly slogans, people's awareness of environmental protection is gradually increasing, and at the same time, distributed energy is also developing. Among them, trigeneration of heat, power and cooling is a kind of distributed energy. The use of trigeneration of heat, power and cooling can not only effectively improve the environment and save energy, but also better meet people's demand for electricity. It is also one of the necessary means for cities to control air pollution and improve the comprehensive utilization rate of energy. In order to more accurately dispatch the combined heat and cooling system and avoid problems such as energy waste and excessive energy, it is particularly important to predict the cooling load.
[0003] In the related technology, historical cooling load data and corresponding weather data are directly obtained, the correlation between the historical cooling load data and weather data is analyzed, and finally the cooling load data is predicted based on the weather data in the future date; however, in the actual cooling process, the prediction of the cooling load is not only affected by the weather, so predicting the cooling load based only on weather data may result in poor accuracy of the cooling load. Summary of the invention
[0004] In order to solve the above technical problems, the present invention provides a cooling and heating combined supply system and method.
[0005] In the first aspect, the present application provides a combined cooling and heating system, which adopts the following technical solution: A combined cooling and heating system includes a meteorological collection module, a human flow collection module, a building information collection module, a cooling load prediction module, a data fusion processing module and an equipment adjustment module, wherein: The meteorological collection module is used to collect current meteorological data in the current period in the target area and send the current meteorological data to the cooling load prediction module; The crowd flow collection module is used to collect the current crowd flow of each building in the target area and send the current crowd flow to the cooling load prediction module, where the current crowd flow is the crowd flow in the current period; A cooling load prediction module, used for obtaining the current cooling load of each building, and determining the cooling load of the building at the next moment according to the current meteorological data, the current flow of people and the current cooling load of the building, and sending the cooling load of the building at the next moment to the data fusion processing module; The building information collection module is used to collect the building service life and send the building service life to the data fusion processing module; The data fusion processing module is used to correct the building cooling load at the next moment according to the service life of the building, obtain the corrected building cooling load, and send the corrected building cooling load to the equipment adjustment module; The equipment adjustment module is used to control the cooling equipment according to the corrected building cooling load.
[0006] In a preferred embodiment of the present application, the cooling load prediction module is used to determine the cooling load of the building at the next moment according to the current meteorological data, the current flow of people and the current cooling load of the building, including: Obtain a first cooling load prediction model and a second cooling load prediction model; Inputting the current meteorological data and the current building cooling load into the first cooling load prediction model to obtain the first building cooling load; Inputting the current flow of people and the current cooling load of the building into the second cooling load prediction model to obtain the cooling load of the second building; The building cooling load at the next moment is determined based on the first building cooling load and the second building cooling load.
[0007] In a preferred embodiment of the present application, the cooling load prediction module may be further configured to determine the cooling load of the building at the next moment based on the cooling load of the first building and the cooling load of the second building, including: Obtain extreme weather data and saturated passenger flow; Determining a first impact weight corresponding to the first building cooling load based on the extreme meteorological data and the current meteorological data; Determining a second impact weight corresponding to the cooling load of the second building based on the saturated human flow and the current human flow; The building cooling load at the next moment is determined according to the first building cooling load, the first impact weight, the second building cooling load and the second impact weight.
[0008] In a preferred embodiment of the present application, the data fusion processing module is used to correct the building cooling load at the next moment according to the service life of the building to obtain the corrected building cooling load, including: Determining a target correction coefficient based on a preset correspondence between the service life and the correction coefficient and the service life of the building; The building cooling load at the next moment is corrected according to the target correction coefficient to obtain the corrected building cooling load.
[0009] In a second aspect, the present application provides a method for combined cooling and heating, which adopts the following technical solution: Collect current meteorological data for the current period in the target area; Collecting the current flow of people in each building in the target area, where the current flow of people is the flow of people in the current time period; Obtaining the current building cooling load of each building, and determining the building cooling load at the next moment according to the current meteorological data, the current flow of people and the current building cooling load; Collect the building's useful life; Correcting the building cooling load at the next moment according to the service life of the building to obtain a corrected building cooling load; The cooling equipment is controlled according to the corrected building cooling load.
[0010] In a preferred embodiment, the present application may be further configured as follows: determining the building cooling load at the next moment according to the current meteorological data, the current flow of people and the current building cooling load includes: Obtain a first cooling load prediction model and a second cooling load prediction model; Inputting the current meteorological data and the current building cooling load into the first cooling load prediction model to obtain the first building cooling load; Inputting the current flow of people and the current cooling load of the building into the second cooling load prediction model to obtain the cooling load of the second building; The building cooling load at the next moment is determined based on the first building cooling load and the second building cooling load.
[0011] In a preferred embodiment of the present application, the step of determining the building cooling load at the next moment based on the first building cooling load and the second building cooling load includes: Obtain extreme weather data and saturated passenger flow; Determining a first impact weight corresponding to the first building cooling load based on extreme meteorological data and the current meteorological data; Determining a second impact weight corresponding to the cooling load of the second building based on the saturated human flow and the current human flow; The building cooling load at the next moment is determined according to the first building cooling load, the first impact weight, the second building cooling load and the second impact weight.
[0012] In a preferred embodiment, the present application may be further configured as follows: the cooling load of the building at the next moment is corrected according to the service life of the building to obtain the corrected cooling load of the building, including: Determining a target correction coefficient based on a preset correspondence between the service life and the correction coefficient and the service life of the building; The building cooling load at the next moment is corrected according to the target correction coefficient to obtain the corrected building cooling load.
[0013] In summary, this application includes the following beneficial technical effects: The present invention obtains the current meteorological data and the current flow of people in the target area, and predicts the building cooling load from two dimensions of meteorological factors and current flow of people. The building cooling load is predicted from different dimensions to effectively improve the accuracy of the building cooling load prediction; the service life of the building is then obtained. When the service life of the building is longer, the building cooling load is more likely to change. Therefore, the building cooling load needs to be corrected according to the service life of the building, so as to further improve the accuracy of the building cooling load prediction. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] Figure 1 A schematic diagram of the structure of a combined cooling and heating system provided in an embodiment of the present application; Figure 2 A schematic diagram of a method flow of a combined cooling and heating system provided in an embodiment of the present application. DETAILED DESCRIPTION
[0015] The following is combined with Figure 1 -Attached Figure 2 This application is described in further detail.
[0016] After reading this specification, those skilled in the art may make modifications to this embodiment without any creative contribution as needed, but such modifications are protected by patent law as long as they are within the scope of the claims of this application.
[0017] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.
[0018] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.
[0019] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. Figure 1 The figure is a schematic diagram of the structure of a combined cooling and heating system provided in an embodiment of the present application.
[0020] A combined cooling and heating system comprises a meteorological collection module, a human flow collection module, a building information collection module, a cooling load prediction module, a data fusion processing module and an equipment adjustment module.
[0021] The meteorological collection module is used to collect the meteorological data of the current period in the target area and send the meteorological data to the cooling load prediction module.
[0022] Specifically, the combined heat and cold supply system in the present application is established based on the existing hot water pipe network and the existing hot water pipe network system. The target area can be any sub-area of the area covered by the existing hot water pipe network heating, or it can be a complete heating area, which is pre-set by the technician. The current time period can be two hours, three hours or four hours. Meteorological data includes temperature data and wind speed data, which can be obtained from the meteorological data information library. It can be understood that in the process of cooling, meteorological data affects the cooling load of the building. For example, when the external ambient temperature is high, the cooling capacity of the building increases, and the cooling load of the building also increases accordingly, so it is necessary to collect temperature data and wind speed data.
[0023] The crowd flow collection module is used to collect the current crowd flow of each building in the target area and send the current crowd flow to the cooling load prediction module. The current crowd flow is the crowd flow in the current period.
[0024] Specifically, the current flow of people represents the number of all people in the current time period; the camera collection device of each building can collect the flow of people images in the current time period, and the flow of people collection module determines the current flow of people based on the flow of people images and the preset flow of people recognition algorithm. The embodiment of the present application does not limit the preset flow of people recognition algorithm. When the current flow of people in a building is larger, in order to improve people's experience and comfort, the cooling capacity needs to be increased, and the cooling load also increases.
[0025] The cooling load prediction module is used to obtain the current cooling load of each building, determine the cooling load of the building at the next moment according to the current meteorological data, the current flow of people and the current cooling load of the building, and send the cooling load of the building at the next moment to the data fusion processing module; The cooling load prediction module is used to determine the cooling load of the building at the next moment based on the current meteorological data, the current flow of people and the current cooling load of the building, including: Obtain a first cooling load prediction model and a second cooling load prediction model; Inputting current meteorological data and current building cooling load into a first cooling load prediction model to obtain the first building cooling load; Inputting the current flow of people and the current building cooling load into the second cooling load prediction model to obtain the second building cooling load; The cooling load of the building at a next moment is determined based on the first building cooling load and the second building cooling load.
[0026] Specifically, the first cooling load prediction model and the second cooling load prediction model can be obtained from the preset model information library; the first cooling load prediction model and the second cooling load prediction model are both obtained by training the neural network model with multiple historical data, and the training process of the first cooling load prediction model includes: obtaining a training set, the training set includes multiple sample data and actual cooling load data at the next moment of the sample collection moment; the sample data includes cooling load data, temperature data and wind speed data at the sample collection moment, and the above multiple sample data are used to predict the cooling load using an untrained cooling load prediction model to obtain the sample predicted cooling load corresponding to each of the multiple sample data; the loss value is determined according to the multiple sample predicted cooling loads and the corresponding actual cooling loads, and the untrained cooling load prediction model is iteratively trained according to the loss value and each sample data until the loss value reaches the preset loss threshold, and the trained cooling load prediction model, i.e., the first cooling load prediction model, is obtained. The embodiment of the present application does not limit the preset loss threshold. The training process of the second cooling load prediction model is the same as the training process of the first cooling load prediction model, and the present application will not repeat it. Furthermore, the current meteorological data and the current building cooling load are input into the first cooling load prediction model, and the first cooling load prediction model outputs the building cooling load at the next moment; the current flow of people and the current building cooling load are then input into the second cooling load prediction model to obtain the second building cooling load. The above neural network model can be an RNN recurrent neural network. Compared with other neural network models, the RNN neural recurrent network can realize the memory function, process the entire sequence and generalize the sequence, and handle the problems related to the building cooling load and the time series very well, so as to effectively improve the accuracy of the first building cooling load and the second building cooling load prediction.
[0027] Further, based on the cooling load of the first building and the cooling load of the second building, determining the cooling load of the building at the next moment includes: Obtain extreme weather data and saturated passenger flow; Determining a first impact weight corresponding to a first building cooling load based on extreme meteorological data and current meteorological data; Determine a second impact weight corresponding to the cooling load of the second building based on the saturated passenger flow and the current passenger flow; The building cooling load at the next moment is determined according to the first building cooling load, the first impact weight, the second building cooling load and the second impact weight.
[0028] Specifically, extreme meteorological data and saturated human flow can be obtained from a preset meteorological database and a human flow library, respectively, which are preset by technicians. Extreme meteorological data include extreme temperature data and extreme wind speed data. In the embodiment of the present application, extreme meteorological data is meteorological data corresponding to severe weather. It can be understood that when it is extremely severe weather, the building cooling load is more susceptible to weather factors; the process of determining the first impact weight includes: determining the temperature difference according to the extreme temperature data and the temperature data, that is, the temperature difference = extreme temperature data - temperature data, and determining the first sub-influence weight corresponding to the temperature difference according to the preset correspondence between the temperature difference and the impact weight; then determining the second sub-influence weight corresponding to the wind speed difference, the determination process of the second sub-influence weight is the same as the determination process of the first sub-influence weight, and the embodiment of the present application will not be repeated; calculating the sum of the weights of the first sub-influence weight and the second sub-influence weight, and determining the sum of the weights as the first impact weight. Determine the difference in human flow according to the saturated human flow and the current human flow, and determine the second impact weight according to the difference in human flow and the preset correspondence, and the preset correspondence is the correspondence between the difference in human flow and the impact weight. The building cooling load at the next moment can be determined according to the calculation formula, which is: building cooling load at the next moment = first building cooling load * first impact weight + second building cooling load * second impact weight. The cooling load prediction module then sends the building cooling load at the next moment to the data fusion processing module so that the data fusion processing module processes multiple data.
[0029] The building information collection module is used to collect the building service life and send the building service life to the data fusion processing module; Specifically, the building service life can be obtained from the building information database.
[0030] The data fusion processing module is used to correct the building cooling load at the next moment according to the received building service life, obtain the corrected building cooling load, and send the corrected building cooling load to the equipment adjustment module; The data fusion processing module is used to correct the building cooling load at the next moment according to the received building service life, and obtain the corrected building cooling load, including: According to the correspondence between the preset service life and the correction coefficient and the service life of the building, the target correction coefficient is determined. The building cooling load at the next moment is corrected according to the target correction coefficient to obtain the corrected building cooling load.
[0031] Specifically, the correspondence between the preset service life and the correction coefficient is pre-set by the technicians based on their work experience. It is understandable that when the service life of the building is older, the quality of the building is worse, and the building performance is also reduced. At this time, the cooling load of the building is more easily affected. Therefore, the service life of the building is obtained so that the building cooling load can be corrected from the dimension of the service life of the building to obtain a more accurate building cooling load at the next moment. Corrected building cooling load = target correction coefficient * building cooling load at the next moment.
[0032] The equipment regulation module is used to control the cooling equipment according to the corrected building cooling load.
[0033] Specifically, the corrected building cooling loads corresponding to all buildings in the target area are obtained, and the comprehensive building cooling load of the target area is obtained. The equipment adjustment module can increase or decrease the output power of different cooling equipment in the cooling network according to the comprehensive building cooling load; the embodiment of the present application does not limit the specific way in which the equipment adjustment module adjusts each cooling equipment, and the user can set it by himself.
[0034] The present invention predicts the cooling load of buildings in the target area and adjusts the cooling equipment in the cooling pipe network, which can not only avoid the problem of insufficient cooling load of buildings failing to meet building needs, but also avoid the occurrence of energy waste caused by excessive cooling load of buildings.
[0035] A second aspect of the present invention provides a method for combined cooling and heating, comprising: Collect current meteorological data for the current period in the target area; Collect the current flow of people in each building in the target area. The current flow of people refers to the flow of people in the current period. Obtain the current building cooling load of each building, and determine the building cooling load at the next moment based on the current meteorological data, current passenger flow and current building cooling load; Collect the building's useful life; Correct the building cooling load at the next moment according to the service life of the building to obtain the corrected building cooling load; Control cooling equipment according to the corrected building cooling load.
[0036] In some embodiments, determining the building cooling load at the next moment according to current meteorological data, current passenger flow, and current building cooling load includes: Obtain a first preset cooling load prediction model and a second preset cooling load prediction model; Inputting current meteorological data and current building cooling load into a first preset cooling load prediction model to obtain a first building cooling load; Inputting the current flow of people and the current building cooling load into a second preset cooling load prediction model to obtain the second building cooling load; The cooling load of the building at a next moment is determined based on the first building cooling load and the second building cooling load.
[0037] In some embodiments, determining the building cooling load at the next moment according to the current meteorological data, the current flow of people and the current building cooling load specifically includes: Obtain extreme weather data and saturated passenger flow; Determining a first impact weight corresponding to a first building cooling load based on extreme meteorological data and current meteorological data; Determine a second impact weight corresponding to the cooling load of the second building based on the saturated passenger flow and the current passenger flow; The building cooling load at the next moment is determined according to the first building cooling load, the first impact weight, the second building cooling load and the second impact weight.
[0038] In some embodiments, the cooling load of the building at the next moment is corrected according to the service life of the building to obtain the corrected cooling load of the building, including: Determine the target correction factor based on the preset correspondence between the service life and the correction factor and the service life of the building; The building cooling load at the next moment is corrected according to the target correction coefficient to obtain the corrected building cooling load.
[0039] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.
Claims
1. A combined cooling and heating system, characterized in that: It includes weather collection module, people flow collection module, building information collection module, cooling load prediction module, data fusion processing module and equipment adjustment module, among which: The meteorological collection module is used to collect current meteorological data in the current period in the target area and send the current meteorological data to the cooling load prediction module; The crowd flow collection module is used to collect the current crowd flow of each building in the target area and send the current crowd flow to the cooling load prediction module, where the current crowd flow is the crowd flow in the current period; A cooling load prediction module, used for obtaining the current cooling load of each building, and determining the cooling load of the building at the next moment according to the current meteorological data, the current flow of people and the current cooling load of the building, and sending the cooling load of the building at the next moment to the data fusion processing module; The building information collection module is used to collect the building service life and send the building service life to the data fusion processing module; The data fusion processing module is used to correct the building cooling load at the next moment according to the service life of the building, obtain the corrected building cooling load, and send the corrected building cooling load to the equipment adjustment module; The equipment adjustment module is used to control the cooling equipment according to the corrected building cooling load.
2. The combined cooling and heating system according to claim 1, characterized in that: The cooling load prediction module is used to determine the cooling load of the building at the next moment according to the current meteorological data, the current flow of people and the current cooling load of the building, including: Obtain a first cooling load prediction model and a second cooling load prediction model; Inputting the current meteorological data and the current building cooling load into the first cooling load prediction model to obtain the first building cooling load; Inputting the current flow of people and the current cooling load of the building into the second cooling load prediction model to obtain the cooling load of the second building; The building cooling load at the next moment is determined based on the first building cooling load and the second building cooling load.
3. The combined cooling and heating system according to claim 2, characterized in that: The cooling load prediction module determines the cooling load of the building at the next moment based on the cooling load of the first building and the cooling load of the second building, including: Obtain extreme weather data and saturated passenger flow; Determining a first impact weight corresponding to the first building cooling load based on the extreme meteorological data and the current meteorological data; Determining a second impact weight corresponding to the cooling load of the second building based on the saturated human flow and the current human flow; The building cooling load at the next moment is determined according to the first building cooling load, the first impact weight, the second building cooling load and the second impact weight.
4. The combined cooling and heating system according to claim 1, characterized in that: The data fusion processing module is used to correct the building cooling load at the next moment according to the service life of the building to obtain the corrected building cooling load, including: Determining a target correction coefficient based on a preset correspondence between the service life and the correction coefficient and the service life of the building; The building cooling load at the next moment is corrected according to the target correction coefficient to obtain the corrected building cooling load.
5. A combined cooling and heating method, implemented based on the combined cooling and heating system of claim 1, characterized in that: The following steps are involved: Collect current meteorological data for the current period in the target area; Collecting the current flow of people in each building in the target area, where the current flow of people is the flow of people in the current time period; Obtaining the current building cooling load of each building, and determining the building cooling load at the next moment according to the current meteorological data, the current flow of people and the current building cooling load; Collect the building's useful life; Correcting the building cooling load at the next moment according to the service life of the building to obtain a corrected building cooling load; The cooling equipment is controlled according to the corrected building cooling load.
6. The method for combined cooling and heating according to claim 5, characterized in that: The determining the building cooling load at the next moment according to the current meteorological data, the current flow of people and the current building cooling load comprises: Obtain a first cooling load prediction model and a second cooling load prediction model; Inputting the current meteorological data and the current building cooling load into the first cooling load prediction model to obtain the first building cooling load; Inputting the current flow of people and the current cooling load of the building into the second cooling load prediction model to obtain the cooling load of the second building; The building cooling load at the next moment is determined based on the first building cooling load and the second building cooling load.
7. The method for combined cooling and heating according to claim 6, characterized in that: The determining the building cooling load at the next moment based on the first building cooling load and the second building cooling load includes: Obtain extreme weather data and saturated passenger flow; Determining a first impact weight corresponding to the first building cooling load based on extreme meteorological data and the current meteorological data; Determining a second impact weight corresponding to the cooling load of the second building based on the saturated human flow and the current human flow; The building cooling load at the next moment is determined according to the first building cooling load, the first impact weight, the second building cooling load and the second impact weight.
8. The method for combined cooling and heating according to claim 5, characterized in that: The cooling load of the building at the next moment is corrected according to the service life of the building to obtain the corrected cooling load of the building, including: Determining a target correction coefficient based on a preset correspondence between the service life and the correction coefficient and the service life of the building; The building cooling load at the next moment is corrected according to the target correction coefficient to obtain the corrected building cooling load.