Analysis method and system suitable for annual heating load of urban building

Abstract

The invention discloses an annual heating load analysis method and system suitable for urban buildings, and the method comprises the steps: obtaining and analyzing building energy consumption data related to the heat transfer loss of the urban buildings in previous years, and determining main energy consumption parameters affecting the heat transfer loss of the urban buildings; calculating the heat transfer load of the urban building based on the data corresponding to the main energy consumption parameters to obtain the heat transfer load of the urban building; constructing a climate prediction model based on the historical meteorological data and a preset neural network model for prediction to obtain local annual climate prediction data; dividing the climate prediction data into a plurality of time stages, and determining the influence coefficient of the climate on the heat transfer loss of the building; and adjusting the heat transfer load of the urban building in different time stages based on the influence coefficient, and determining the annual heat supply load of the urban building based on the heat transfer load. According to the method, the actual heat supply requirements of the urban building under different climate conditions can be accurately analyzed, and comprehensive analysis on the heat transfer loss of the urban building is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy management, and particularly to a heating load analysis method and system applicable to urban buildings throughout the year. Background Art

[0002] The heating load analysis of urban buildings is a crucial part in urban energy management and building design. With the acceleration of urbanization and the increasing impact of climate change, accurately evaluating and predicting the heating demand of urban buildings is of great significance for improving energy utilization efficiency, reducing carbon emissions, and enhancing indoor comfort.

[0003] However, traditional methods usually adopt static analysis methods to calculate the annual heating load. They ignore the impact of climate conditions changing in different time periods on the heating load, lack dynamic response to changes in different climate stages, and cannot fully consider the impact of climate change on building heat transfer losses. Climate has different effects on the heat transfer requirements of buildings in different seasons and different weather conditions. Moreover, traditional methods usually lack comprehensive consideration of the impact of climate factors and building energy consumption parameter characteristics on the heating load. Therefore, they cannot accurately and comprehensively evaluate the actual heating demand of urban buildings under different climate conditions. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a heating load analysis method and system applicable to urban buildings throughout the year, including: Obtaining building energy consumption data related to the heat transfer loss of urban buildings in previous years, and analyzing the building energy consumption data to determine the main energy consumption parameters affecting the heat transfer loss of urban buildings; Calculating the heat transfer load of urban buildings based on the building energy consumption data corresponding to the main energy consumption parameters to obtain the heat transfer load of urban buildings; Obtaining local historical meteorological data, and constructing a climate prediction model based on the historical meteorological data and a preset neural network model for prediction to obtain local annual climate prediction data; Conducting a phased analysis of the climate prediction data, dividing the climate prediction data into several time periods, and determining the influence coefficient of climate on building heat transfer loss in each time period; Adjusting the heat transfer load of urban buildings in different time periods based on the influence coefficient, and determining the annual heating load of urban buildings based on the adjusted heat transfer load in each time period.

[0005] Further, the obtaining building energy consumption data related to the heat transfer loss of urban buildings in previous years, and analyzing the building energy consumption data to determine the main energy consumption parameters affecting the heat transfer loss of urban buildings includes: Obtain building energy consumption data related to the heat transfer loss of urban buildings in previous years, and divide the building energy consumption data into several energy consumption parameter data groups according to the parameter type; Input each energy consumption parameter data group into the building heat transfer loss simulation model separately, and determine the amplitude of the heat transfer loss change corresponding to each energy consumption parameter data group; Select the parameters corresponding to the energy consumption parameter data groups with the amplitude of the heat transfer loss change greater than the preset threshold in the energy consumption parameter data groups, and determine them as the main energy consumption parameters affecting the heat transfer loss of urban buildings.

[0006] Furthermore, calculating the heat transfer load of the urban building based on the building energy consumption data corresponding to the main energy consumption parameters to obtain the heat transfer load of the urban building, including: Determine the building energy consumption data corresponding to the main energy consumption parameters from the building energy consumption data, and calculate the average value of the building energy consumption data corresponding to each main energy consumption parameter; Determine the average value of the building energy consumption data corresponding to each main energy consumption parameter as the heat transfer load corresponding to each main energy consumption parameter, and add up the heat transfer loads corresponding to all the main energy consumption parameters to calculate the heat transfer load of the urban building.

[0007] Furthermore, obtaining the local historical meteorological data, and constructing a climate prediction model based on the historical meteorological data and a preset neural network model for prediction to obtain the local annual climate prediction data, including: Obtain the local historical meteorological data, and preprocess the historical meteorological data. The preprocessing includes removing incorrect data points and duplicate data points, filling in missing data points, and standardizing the data; Extract features from the preprocessed historical meteorological data, construct a data set based on the extracted features, and input the data set into the preset neural network model to construct an initial climate prediction model; Divide the data set into a training set and a test set according to a preset ratio, and input the training set and the test set into the initial climate prediction model; Train and test the initial climate prediction model until the initial climate prediction model meets the preset convergence condition to obtain the local annual climate prediction model; Obtain the local meteorological monitoring data, and input the meteorological monitoring data into the climate prediction model for prediction to obtain the local annual climate prediction data.

[0008] Furthermore, performing a phased analysis on the climate prediction data, and dividing the climate prediction data into several time stages, including: Obtain the local annual climate prediction data, and divide the climate prediction data into several meteorological parameter data groups according to the parameter type; Calculate the first correlation degree between each meteorological parameter data group and the building energy consumption data, and determine the meteorological parameter data groups with the first correlation degree greater than the preset threshold as the key meteorological parameter data groups; Based on each key meteorological parameter data group, construct the meteorological parameter change curve of the time progress corresponding to each key meteorological parameter, and determine the seasonal alternation time nodes in the meteorological parameter change curve; Determine the time nodes corresponding to the maximum curve mutation nodes within the preset time range near each seasonal alternation time node in each meteorological parameter change curve, and determine the parameter mutation time nodes corresponding to the seasonal alternation time nodes of each key meteorological parameter; Calculate the average value of the parameter mutation time nodes corresponding to all key meteorological parameters at the same seasonal alternation time node, obtain the comprehensive parameter mutation time node of all key meteorological parameters at the same seasonal alternation time node, and divide the climate prediction data into several time stages according to each comprehensive parameter mutation time node.

[0009] Further, the determining the influence coefficient of the climate on the building heat transfer loss in each time stage includes: Determine the key meteorological parameter data groups corresponding to each time stage, calculate the second correlation degree between the key meteorological parameter data groups corresponding to each time stage and the building energy consumption data, and use the second correlation degree as the sub-influence coefficient of each key meteorological parameter corresponding to each time stage; Obtain the preset weights corresponding to each key meteorological parameter, perform weighted summation calculation on the sub-influence coefficients of each key meteorological parameter in each time stage and the corresponding preset weights, and obtain the influence coefficient of the climate on the building heat transfer loss in each time stage.

[0010] Further, the adjusting the heat transfer load of urban buildings in different time stages based on the influence coefficient, and determining the annual heating load of urban buildings based on the adjusted heat transfer load of each time stage includes: Determine the influence coefficient of the climate on the building heat transfer loss in each time stage, and perform multiplication calculation on the influence coefficient and the heat transfer load to obtain the adjusted heat transfer load of each time stage; Perform summation calculation on the adjusted heat transfer loads of all time stages to obtain the annual heating load of urban buildings.

[0011] The present invention also provides a heating load analysis system applicable to urban buildings throughout the year, including: An acquisition module, configured to acquire the building energy consumption data related to the heat transfer loss of urban buildings in previous years, analyze the building energy consumption data, and determine the main energy consumption parameters affecting the heat transfer loss of urban buildings; A calculation module, configured to calculate the heat transfer load of urban buildings based on the building energy consumption data corresponding to the main energy consumption parameters, so as to obtain the heat transfer load of urban buildings; A prediction module, configured to obtain the local historical meteorological data, and construct a climate prediction model based on the historical meteorological data and a preset neural network model for prediction, so as to obtain the local annual climate prediction data; A division module, configured to perform a phased analysis on the climate prediction data, divide the climate prediction data into several time phases, and determine the influence coefficient of the climate on the building heat transfer loss in each time phase; A determination module, configured to adjust the heat transfer load of urban buildings in different time phases based on the influence coefficient, and determine the annual heating load of urban buildings based on the adjusted heat transfer load in each time phase.

[0012] Compared with the prior art, the beneficial effects of an embodiment of a heating load analysis method and system applicable to urban buildings throughout the year according to the present invention are as follows: By analyzing the building energy consumption data, the present invention determines the main energy consumption parameters affecting the heat transfer loss of urban buildings, enabling a better understanding and quantification of the energy consumption characteristics of buildings, and realizing a comprehensive and integrated analysis of the heat transfer loss of urban buildings; By calculating the heat transfer load of urban buildings, the present invention can more accurately evaluate the energy demand of buildings, providing an important basis for the design and operation of heating systems; By performing a phased analysis on the climate prediction data, the present invention determines the influence coefficient of the climate on the building heat transfer loss in different time phases, which helps to understand the impact of climate change on building energy consumption and can accurately analyze the actual heating demand of urban buildings under different climate conditions; Based on the adjusted heat transfer load data, the present invention determines the annual heating load of urban buildings, providing guidance for the planning and operation of urban heating systems. Description of the Drawings

[0013] Figure 1 is a schematic structural diagram of the process of a heating load analysis method applicable to urban buildings throughout the year in an embodiment of the present invention; Figure 2 is a schematic diagram of the composition of a heating load analysis system applicable to urban buildings throughout the year in an embodiment of the present invention. Detailed Embodiments

[0014] The following further describes in detail the specific embodiments of the present application in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not used to limit the scope of the present invention.

[0015] In the description of the present application, it should be understood that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the platform or component referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation to the present application.

[0016] The terms "first", "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second" may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise stated, the meaning of "a plurality" is two or more.

[0017] In the description of the present application, it should be noted that unless otherwise clearly specified and limited, the terms "install", "connect", "couple" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0018] As Figure 1 shown, in an embodiment of the present application, a heating load analysis method applicable to urban buildings throughout the year is provided, including: S100: Obtain building energy consumption data related to the heat transfer loss of urban buildings in previous years, and analyze the building energy consumption data to determine the main energy consumption parameters affecting the heat transfer loss of urban buildings; S200: Calculate the heat transfer load of urban buildings based on the building energy consumption data corresponding to the main energy consumption parameters to obtain the heat transfer load of urban buildings; S300: Obtain the local historical meteorological data, and construct a climate prediction model based on the historical meteorological data and a preset neural network model for prediction to obtain the local annual climate prediction data; S400: Conduct a phased analysis of the climate prediction data, divide the climate prediction data into several time stages, and determine the influence coefficient of the climate on the building heat transfer loss in each time stage; S500: Adjust the heat transfer load of urban buildings in different time stages based on the influence coefficient, and determine the annual heating load of urban buildings based on the adjusted heat transfer load in each time stage.

[0019] Furthermore, by analyzing the building energy consumption data, the present invention determines the main energy consumption parameters affecting the heat transfer loss of urban buildings, enabling a better understanding and quantification of the energy consumption characteristics of buildings, and achieving a comprehensive and integrated analysis of the heat transfer loss of urban buildings; by calculating the heat transfer load of urban buildings, the present invention can more accurately evaluate the energy demand of buildings, providing an important basis for the design and operation of heating systems; by performing a phased analysis of climate prediction data, the present invention determines the influence coefficient of climate on building heat transfer loss at different time stages, which helps to understand the impact of climate change on building energy consumption and can accurately analyze the actual heating demand of urban buildings under different climate conditions; based on the adjusted heat transfer load data, the present invention determines the annual heating load of urban buildings, providing guidance for the planning and operation of urban heating systems.

[0020] In an embodiment of the present application, a method for analyzing the annual heating load applicable to urban buildings is provided. The method includes obtaining building energy consumption data related to the heat transfer loss of urban buildings in previous years, and analyzing the building energy consumption data to determine the main energy consumption parameters affecting the heat transfer loss of urban buildings, including: obtaining building energy consumption data related to the heat transfer loss of urban buildings in previous years, and dividing the building energy consumption data into several energy consumption parameter data groups according to the parameter type; separately inputting each energy consumption parameter data group into a building heat transfer loss simulation model to determine the amplitude of the change in heat transfer loss corresponding to each energy consumption parameter data group; selecting the parameters corresponding to the energy consumption parameter data groups with the amplitude of the change in heat transfer loss greater than a preset threshold in the energy consumption parameter data groups, and determining them as the main energy consumption parameters affecting the heat transfer loss of urban buildings.

[0021] Specifically, by collecting building energy consumption data over the years and dividing the building energy consumption data into several energy consumption parameter data groups according to parameter types, such as building structure parameters, equipment efficiency parameters, insulation performance parameters, etc., the building energy consumption data can be better classified and analyzed; a simulation model of building heat transfer loss is set in advance, which can simulate the heat transfer situation of the building under different climate conditions, calculate and analyze the heat transfer loss according to the building energy consumption data and parameters, input each energy consumption parameter data group into the building heat transfer loss simulation model separately, determine the amplitude of the heat transfer loss change corresponding to each energy consumption parameter data group, and analyze the influence degree of each parameter on the heat transfer loss; select the energy consumption parameter data groups with the amplitude of heat transfer loss change greater than the preset threshold in the energy consumption parameter data groups and determine them as the main energy consumption parameters affecting the heat transfer loss of urban buildings. These main parameters have a greater impact on the heat transfer loss of buildings and need to be focused on. This step can deeply analyze the building energy consumption data, determine the main energy consumption parameters affecting the heat transfer loss of urban buildings, and provide an important basis for building energy efficiency improvement and urban heating system optimization; it can help identify the key influencing factors in building energy consumption, make targeted energy efficiency improvements and optimizations, improve the energy utilization efficiency of buildings, reduce operating costs, and reduce the impact on the environment; through the analysis of the main energy consumption parameters, targeted energy management strategies can be formulated, building design and operation can be optimized, the energy performance of urban buildings can be improved, and the sustainable development of the city can be promoted.

[0022] In an embodiment of the present application, a heating load analysis method applicable to urban buildings throughout the year is provided. Calculating the heat transfer load of an urban building based on the building energy consumption data corresponding to the main energy consumption parameters to obtain the heat transfer load of the urban building includes: determining the building energy consumption data corresponding to the main energy consumption parameters from the building energy consumption data and calculating the average value of the building energy consumption data corresponding to each main energy consumption parameter; determining the average value of the building energy consumption data corresponding to each main energy consumption parameter as the heat transfer load corresponding to each main energy consumption parameter, and adding up the heat transfer loads corresponding to all the main energy consumption parameters to obtain the heat transfer load of the urban building.

[0023] Specifically, after determining the main energy consumption parameters from the previous analysis, it is necessary to extract the corresponding data from the building energy consumption data; for each main energy consumption parameter, calculate the average value of the corresponding building energy consumption data to determine the average influence degree of each parameter; determine the average value of the building energy consumption data corresponding to each main energy consumption parameter as the heat transfer load corresponding to each main energy consumption parameter, and convert the building energy consumption data into heat transfer load data; add up the heat transfer loads corresponding to all the main energy consumption parameters to obtain the total heat transfer load of urban buildings, which represents the heating demand of the entire urban building system. By converting the main energy consumption parameters into heat transfer load data in this step, it is possible to more intuitively understand the contribution degree of different parameters to the heat transfer demand of urban buildings, which helps in the evaluation and optimization of building energy efficiency; it can help urban energy managers better understand the overall heating demand of urban buildings and formulate reasonable heating plans and energy policies accordingly to achieve the optimization of energy utilization; by analyzing the heat transfer load data of the main energy consumption parameters, it can provide important references for the design, planning, and operation of the urban heating system, improve the efficiency and reliability of the system, and promote the sustainable development of urban energy.

[0024] In an embodiment of the present application, a heating load analysis method applicable to urban buildings throughout the year is provided. Obtain the local historical meteorological data, and based on the historical meteorological data and a preset neural network model, construct a climate prediction model for prediction to obtain the local annual climate prediction data, including: obtaining the local historical meteorological data, and preprocessing the historical meteorological data. The preprocessing includes removing incorrect data points and duplicate data points, filling in missing data points, and standardizing the data; extracting features from the preprocessed historical meteorological data, constructing a data set based on the extracted features, and inputting the data set into the preset neural network model to construct an initial climate prediction model; dividing the data set into a training set and a test set according to a preset ratio, and inputting the training set and the test set into the initial climate prediction model; training and testing the initial climate prediction model until the initial climate prediction model meets the preset convergence condition to obtain the local annual climate prediction model; obtaining the local meteorological monitoring data, and inputting the meteorological monitoring data into the climate prediction model for prediction to obtain the local annual climate prediction data.

[0025] Specifically, obtain the local historical meteorological data, including information such as temperature, humidity, and wind speed. Preprocess the data, including removing error data points and duplicate data points, filling in missing data, and performing data standardization to ensure data quality and consistency. Extract features from the preprocessed historical meteorological data, extract features that have an important impact on climate prediction, such as seasonal changes and correlations between meteorological indicators. Then, construct a data set based on these features for training and testing the climate prediction model. Input the constructed data set into a preset neural network model to construct an initial climate prediction model. Divide the data set into a training set and a test set according to a preset ratio, use the training set to train the model, and then use the test set to evaluate the performance of the model. Iteratively train and test the model until the preset convergence condition is met to obtain a local annual climate prediction model. Obtain the local real-time meteorological monitoring data, input the data into the trained climate prediction model for prediction, and obtain the local annual climate prediction data. Through this step of constructing a climate prediction model, historical meteorological data can be used to predict future climate, improving the accuracy and reliability of meteorological prediction. In an embodiment of the present application, a heating load analysis method applicable to urban buildings throughout the year is provided. The climate prediction data is analyzed in stages, and the climate prediction data is divided into several time stages, including: obtaining the local annual climate prediction data and dividing the climate prediction data into several meteorological parameter data groups according to parameter types; calculating the first correlation degree between each meteorological parameter data group and the building energy consumption data, and determining the meteorological parameter data group with the first correlation degree greater than the preset threshold as the key meteorological parameter data group; constructing a meteorological parameter change curve of the time progress corresponding to each key meteorological parameter based on each key meteorological parameter data group, and determining the seasonal alternation time nodes in the meteorological parameter change curve; determining the time node corresponding to the maximum curve mutation node within a preset time range near each seasonal alternation time node in each meteorological parameter change curve, and determining this time node as the parameter mutation time node corresponding to the seasonal alternation time node of each key meteorological parameter; calculating the average value of the parameter mutation time nodes corresponding to all key meteorological parameters at the same seasonal alternation time node to obtain the comprehensive parameter mutation time node of all key meteorological parameters at the same seasonal alternation time node, and dividing the climate prediction data into several time stages according to each comprehensive parameter mutation time node.

[0026] Specifically, the meteorological prediction data is divided into different meteorological parameter data groups according to parameter types, and then the first correlation degree between each meteorological parameter data group and the building energy consumption data is calculated. The correlation degree represents the degree of association between the meteorological parameter and the building energy consumption. The meteorological parameter data groups with the first correlation degree greater than the preset threshold are determined as key meteorological parameter data groups, and these parameters have an important impact on the building energy consumption. For each key meteorological parameter data group, a meteorological parameter change curve corresponding to the time progress is constructed to reflect the change trend of the parameter over time. The seasonal alternation time nodes are determined in the meteorological parameter change curve. In the preset time range near the seasonal alternation time nodes, the maximum mutation node in the meteorological parameter change curve is found and determined as the parameter mutation time node corresponding to the seasonal alternation time node of each key meteorological parameter. The average value of the parameter mutation time nodes corresponding to the same seasonal alternation time node of all key meteorological parameters is calculated to determine the comprehensive parameter mutation time node of all key meteorological parameters at the same seasonal alternation time node. The climate prediction data is divided into several time stages according to the comprehensive parameter mutation time node, and each stage represents the overall change trend of the meteorological parameters. This step can better understand the influence law of meteorological parameters on building energy consumption by identifying key meteorological parameters and seasonal alternation time nodes, which helps to optimize the building energy management and the formulation of energy-saving measures. Determining the comprehensive parameter mutation time node and dividing the time stages helps to predict the change trend of meteorological parameters in different time periods, providing a more accurate basis for the prediction and planning of building energy consumption. It can help professionals in the building field better understand the impact of climate change on building energy consumption, improve building energy efficiency and reduce energy consumption, and promote the development of sustainable buildings.

[0027] In an embodiment of the present application, a heating load analysis method applicable to urban buildings throughout the year is provided. The determining the influence coefficient of the climate on the building heat transfer loss in each time stage includes: determining each key meteorological parameter data group corresponding to each time stage, and calculating the second correlation degree between each key meteorological parameter data group corresponding in each time stage and the building energy consumption data, and taking the second correlation degree as the sub-influence coefficient of each key meteorological parameter corresponding to each time stage; obtaining the preset weights corresponding to each key meteorological parameter, and performing weighted addition calculation on the sub-influence coefficient of each key meteorological parameter in each time stage and the corresponding preset weight to obtain the influence coefficient of the climate on the building heat transfer loss in each time stage.

[0028] Specifically, for each time period, calculate the second correlation degree between each group of key meteorological parameter data and the building energy consumption data, and use it as the sub-influence coefficient of each key meteorological parameter, which represents the influence degree of each key meteorological parameter on the building energy consumption during this time period; obtain the preset weights corresponding to each key meteorological parameter that are preset, and these weights reflect the importance of each meteorological parameter in the building heat transfer loss; perform weighted summation calculation on the sub-influence coefficient of each key meteorological parameter in each time period and the corresponding preset weight to obtain the influence coefficient of the climate on the building heat transfer loss in each time period. This influence coefficient comprehensively considers the influence degree of each key meteorological parameter during this time period and its weight in the building heat transfer loss. By calculating the influence coefficient of each key meteorological parameter on the building energy consumption in different time periods in this step, the influence degree of climate factors on the building heat transfer loss can be evaluated more accurately, guiding the formulation of building energy management and energy-saving measures; combining the preset weights to perform weighted summation calculation on the influence coefficient of each key meteorological parameter makes the building energy consumption prediction more targeted and accurate, helping to improve the building energy utilization efficiency and reduce energy waste.

[0029] In an embodiment of the present application, a heating load analysis method applicable to urban buildings throughout the year is provided. The heat transfer load of urban buildings in different time periods is adjusted based on the influence coefficient, and the annual heating load of urban buildings is determined based on the adjusted heat transfer load of each time period, including: determining the influence coefficient of the climate on the building heat transfer loss in each time period, and performing multiplication calculation on the influence coefficient and the heat transfer load to obtain the adjusted heat transfer load of each time period; adding up the adjusted heat transfer loads of all time periods to obtain the annual heating load of urban buildings.

[0030] Specifically, in each time period, the influence coefficient of climate on the building heat transfer loss has been calculated, which reflects the influence degree of climate factors on the heat transfer loss; multiplying the influence coefficient of each time period by the heat transfer load to obtain the adjusted heat transfer load for each time period. This step adjusts the heat transfer load according to the influence degree of climate factors on the heat transfer loss to make it more in line with the actual climate conditions; adding up the adjusted heat transfer loads of all time periods to obtain the annual heating load of urban buildings. This is a comprehensive assessment of the annual heating demand of urban buildings, considering the influence of climate factors on the heat transfer load in different time periods. By calculating the influence coefficient of climate on the building heat transfer loss in each time period and multiplying it by the heat transfer load, this step can more accurately consider the influence of climate factors on the building heat transfer load, improve the accuracy and predictability of the heat transfer load; adjusting the heat transfer load can enable the building system to more effectively respond to climate change, improve energy utilization efficiency, reduce energy consumption and operating costs; calculating the annual heating load can provide an important reference for the planning and operation of the urban heating system, help to reasonably arrange heating resources and optimize the heating scheme, and improve the stability and efficiency of the urban heating system. This method can help cities achieve energy conservation and emission reduction goals and promote sustainable urban construction and development.

[0031] As Figure 2 shown, in the embodiment of the present application, a heating load analysis system applicable to urban buildings throughout the year is provided, including: an acquisition module for acquiring building energy consumption data related to the heat transfer loss of urban buildings in previous years and analyzing the building energy consumption data to determine the main energy consumption parameters affecting the heat transfer loss of urban buildings; a calculation module for calculating the heat transfer load of urban buildings based on the building energy consumption data corresponding to the main energy consumption parameters to obtain the heat transfer load of urban buildings; a prediction module for acquiring local historical meteorological data and constructing a climate prediction model based on the historical meteorological data and a preset neural network model for prediction to obtain the annual climate prediction data of the local area; a division module for performing stage analysis on the climate prediction data, dividing the climate prediction data into several time periods, and determining the influence coefficient of climate on the building heat transfer loss in each time period; a determination module for adjusting the heat transfer load of urban buildings in different time periods based on the influence coefficient and determining the annual heating load of urban buildings based on the adjusted heat transfer load of each time period.

[0032] In summary, the embodiments of the present invention provide a heating load analysis method and system applicable to urban buildings throughout the year, which include: obtaining and analyzing building energy consumption data related to the heat transfer loss of urban buildings in previous years, and determining the main energy consumption parameters affecting the heat transfer loss of urban buildings; calculating the heat transfer load of urban buildings based on the data corresponding to the main energy consumption parameters to obtain the heat transfer load of urban buildings; constructing a climate prediction model based on historical meteorological data and a preset neural network model to predict the annual climate prediction data of the local area; dividing the climate prediction data into several time stages, and determining the influence coefficient of the climate on the building heat transfer loss; adjusting the heat transfer load of urban buildings in different time stages based on the influence coefficient, and determining the annual heating load of urban buildings based on the adjusted heat transfer load. The present invention can accurately analyze the actual heating demand of urban buildings under different climate conditions, and realizes a comprehensive and integrated analysis of the heat transfer loss of urban buildings.

[0033] Finally, it should be noted that: Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

[0034] The above is only one embodiment of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made in accordance with the present invention, as long as they do not deviate from the essence of the present invention, should be regarded as falling within the protection scope of the present invention and being restricted. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and related descriptions of the above-described platform can refer to the corresponding process in the foregoing platform embodiments, and will not be repeated here.

[0035] The term "including" or any other similar term is intended to cover non-exclusive inclusion, so that a process, platform, article, or device / platform including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to these processes, platforms, articles, or devices / platforms.

[0036] So far, the technical solutions of the present invention have been described in combination with the further embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to closely related technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

[0037] The above is only the preferred embodiment of the present invention, and is not used to limit the protection scope of the present invention.

Claims

1. A heating load analysis method applicable to urban buildings throughout the year, characterized in that, Including: Obtain building energy consumption data related to the heat transfer loss of urban buildings in previous years, analyze the building energy consumption data, and determine the main energy consumption parameters affecting the heat transfer loss of urban buildings; Calculate the heat transfer load of urban buildings based on the building energy consumption data corresponding to the main energy consumption parameters, and obtain the heat transfer load of urban buildings; Obtain the local historical meteorological data, and construct a climate prediction model based on the historical meteorological data and a preset neural network model for prediction to obtain the local annual climate prediction data; Conduct a phased analysis of the climate prediction data, divide the climate prediction data into several time stages, and determine the influence coefficient of the climate on the building heat transfer loss in each time stage; Adjust the heat transfer load of urban buildings in different time stages based on the influence coefficient, and determine the annual heating load of urban buildings based on the adjusted heat transfer load in each time stage.

2. A heating load analysis method applicable to urban buildings throughout the year according to claim 1, characterized in that, The obtaining of the building energy consumption data related to the heat transfer loss of urban buildings in previous years, and the analysis of the building energy consumption data to determine the main energy consumption parameters affecting the heat transfer loss of urban buildings includes: Obtain the building energy consumption data related to the heat transfer loss of urban buildings in previous years, and divide the building energy consumption data into several energy consumption parameter data groups according to the parameter type; Input each energy consumption parameter data group into the building heat transfer loss simulation model separately, and determine the amplitude of the heat transfer loss change corresponding to each energy consumption parameter data group; Select the parameters corresponding to the energy consumption parameter data groups with the amplitude of the heat transfer loss change greater than the preset threshold in the energy consumption parameter data groups, and determine them as the main energy consumption parameters affecting the heat transfer loss of urban buildings.

3. A heating load analysis method applicable to urban buildings throughout the year according to claim 2, characterized in that, The calculating of the heat transfer load of urban buildings based on the building energy consumption data corresponding to the main energy consumption parameters to obtain the heat transfer load of urban buildings includes: Determine the building energy consumption data corresponding to the main energy consumption parameters from the building energy consumption data, and calculate the average value of the building energy consumption data corresponding to each main energy consumption parameter; Determine the average value of the building energy consumption data corresponding to each main energy consumption parameter as the heat transfer load corresponding to each main energy consumption parameter, and add up the heat transfer loads corresponding to all main energy consumption parameters to calculate the heat transfer load of urban buildings.

4. A heating load analysis method applicable to urban buildings throughout the year according to claim 3, characterized in that, The obtaining of the local historical meteorological data, and the construction of a climate prediction model based on the historical meteorological data and a preset neural network model for prediction to obtain the local annual climate prediction data includes: Obtain the local historical meteorological data, and preprocess the historical meteorological data. The preprocessing includes removing error data points and duplicate data points, filling in missing data points, and standardizing the data; Extract features from the preprocessed historical meteorological data, construct a data set based on the extracted features, and input the data set into the preset neural network model to construct an initial climate prediction model; Divide the data set into a training set and a test set according to a preset ratio, and input the training set and the test set into the initial climate prediction model; Train and test the initial climate prediction model until the initial climate prediction model meets the preset convergence condition to obtain the local annual climate prediction model; Obtain local meteorological monitoring data and input the meteorological monitoring data into a climate prediction model for prediction to obtain local annual climate prediction data.

5. A heating load analysis method applicable to urban buildings throughout the year according to claim 4, characterized in that Perform phased analysis on the climate prediction data, and divide the climate prediction data into several time phases, including: Obtain local annual climate prediction data and divide the climate prediction data into several meteorological parameter data groups according to parameter types; Calculate the first correlation degree between each meteorological parameter data group and the building energy consumption data, and determine the meteorological parameter data groups with the first correlation degree greater than the preset threshold as the key meteorological parameter data groups; Based on each key meteorological parameter data group, construct a meteorological parameter change curve of the time progress corresponding to each key meteorological parameter, and determine the seasonal alternation time nodes in the meteorological parameter change curve; Determine the time nodes corresponding to the maximum curve mutation nodes within a preset time range near each seasonal alternation time node in each meteorological parameter change curve, and determine the parameter mutation time nodes corresponding to the seasonal alternation time nodes of each key meteorological parameter; Calculate the average value of the parameter mutation time nodes corresponding to all key meteorological parameters at the same seasonal alternation time node to obtain the comprehensive parameter mutation time node of all key meteorological parameters at the same seasonal alternation time node, and divide the climate prediction data into several time phases according to each comprehensive parameter mutation time node.

6. A heating load analysis method applicable to urban buildings throughout the year according to claim 5, characterized in that, The determination of the influence coefficient of the climate on the building heat transfer loss in each time phase includes: Determine the key meteorological parameter data groups corresponding to each time phase, and calculate the second correlation degree between the key meteorological parameter data groups corresponding to each time phase and the building energy consumption data, and use the second correlation degree as the sub-influence coefficient of each key meteorological parameter corresponding to each time phase; Obtain the preset weights corresponding to each key meteorological parameter, and perform weighted summation calculation on the sub-influence coefficients of each key meteorological parameter in each time phase and the corresponding preset weights to obtain the influence coefficient of the climate on the building heat transfer loss in each time phase.

7. A heating load analysis method applicable to urban buildings throughout the year according to claim 6, characterized in that, Adjust the heat transfer load of urban buildings in different time phases based on the influence coefficient, and determine the annual heating load of urban buildings based on the adjusted heat transfer load of each time phase, including: Determine the influence coefficient of the climate on the building heat transfer loss in each time phase, and perform multiplication calculation on the influence coefficient and the heat transfer load to obtain the adjusted heat transfer load of each time phase; Perform summation calculation on the adjusted heat transfer loads of all time phases to obtain the annual heating load of urban buildings.

8. A heating load analysis system applicable to urban buildings throughout the year, characterized in that, Including: An acquisition module for acquiring building energy consumption data related to the heat transfer loss of urban buildings in previous years, analyzing the building energy consumption data, and determining the main energy consumption parameters affecting the heat transfer loss of urban buildings; A calculation module for calculating the heat transfer load of urban buildings based on the building energy consumption data corresponding to the main energy consumption parameters to obtain the heat transfer load of urban buildings; A prediction module for acquiring local historical meteorological data, and constructing a climate prediction model based on the historical meteorological data and a preset neural network model for prediction to obtain local annual climate prediction data; A partitioning module, which is used to conduct phased analysis on climate prediction data, partition the climate prediction data into several time phases, and determine the influence coefficient of climate on the heat transfer loss of buildings in each time phase; A determination module, which is used to adjust the heat transfer load of urban buildings in different time phases based on the influence coefficient, and determine the annual heating load of urban buildings based on the adjusted heat transfer load in each time phase.

Images