Air conditioning load regulation method for distribution room based on model prediction

Abstract

The present invention discloses a method for regulating the air-conditioning load in a distribution room based on model prediction. The thermodynamic model of the distribution station is designed from several important aspects, including spatial structure, temporal structure and grid load demand. The method fully considers the influence of the spatial size and structure of the distribution station on the heat energy transfer in the distribution station, and also considers the problem of the distribution station absorbing heat energy based on the time period. The method also considers the influence of the grid load on the heat loss of the distribution equipment, thereby realizing comprehensive and effective supervision of the influence of heat energy in the distribution station. At the same time, with the intelligent development of the power grid, the distribution equipment in the distribution station is also gradually increasing. The design of this case can also take into account the increase in equipment in the distribution station, adjust the spatial structure characteristics of the distribution station according to the added distribution equipment, and increase the surface temperature monitoring of the distribution equipment.

Description

Technical Field

[0001] The present invention relates to a method for regulating the air-conditioning load in a power distribution room based on model prediction, and belongs to a load control technology for a power distribution station. Background Art

[0002] With the rapid development of the economy and the electric power industry, the number of power-consuming facilities and distribution stations, which are closely related to people's work and lives, has increased significantly, bringing significant convenience to people's electricity use. Due to the increasing demand for intelligent power dispatching master station systems, the number of distribution equipment within distribution stations has also increased. This increase in distribution equipment not only increases energy consumption but also raises the temperature within the distribution stations. To ensure the safe and stable operation of distribution stations, greater attention must be paid to the air conditioning control of distribution stations. This requires both safe and stable operation of distribution equipment and predictable energy consumption to reduce line losses and load control across the entire power grid. Therefore, predicting and controlling the air conditioning load at distribution stations has become increasingly important. Summary of the Invention

[0003] Purpose of the invention: In order to overcome the deficiencies in the prior art, the present invention provides a distribution room air-conditioning load regulation method based on model prediction to predict and regulate the air-conditioning load of the distribution station.

[0004] Technical solution: To achieve the above purpose, the technical solution adopted by the present invention is:

[0005] A method for regulating air conditioning load in a power distribution room based on model prediction includes the following steps:

[0006] Step 1: Construct a distribution station spatial structure feature extraction module to extract the distribution station spatial structure feature F space ;

[0007] Step 2: Obtain historical data on heat loss and air conditioning load of power distribution equipment, including time characteristics F time , distribution network net load active demand characteristics F P , distribution station temperature characteristics F tem and air conditioning cooling capacity label Q ture , historical data is divided into training data set and test data set in proportion;

[0008] Step 3: Construct a thermodynamic model of the distribution station based on the neural network; first, use the spatial fusion module to analyze the spatial structure characteristics F of the distribution station. space and the distribution station temperature characteristic F tem Perform feature fusion to obtain spatial fusion features By spatial fusion features Characterize the impact of the spatial structure of the distribution room on the air conditioning load; then use the time fusion module to analyze the time characteristics F timeand the distribution station temperature characteristic F tem Perform feature fusion to obtain time fusion features Fusion of features through time Characterize the impact of time information on air conditioning load; then use the active demand fusion module to fusion the active demand characteristics of the distribution network net load F P and the distribution station temperature characteristic F tem Perform feature fusion to obtain active demand fusion features Fusion characteristics through active power demand Characterize the impact of the net load active demand of the distribution network on the air conditioning load; finally, the spatial fusion characteristics are adjusted by adaptively adjusting the fusion module Temporal fusion features and active power demand fusion characteristics Perform multi-dimensional fusion to obtain multi-dimensional fusion features Multidimensional fusion features Input to the detection head for semantic segmentation to obtain the predicted value Q of air conditioning cooling capacity pre ;

[0009] Step 4: Use the loss function to supervise the training of the distribution station thermodynamic model to obtain a trained distribution station thermodynamic model;

[0010] Step 5: Input the spatial structure characteristics of the distribution station and the test data set into the trained distribution station thermodynamic model to obtain the predicted value of the air conditioning cooling capacity, and adjust the air conditioning load of the distribution room based on the predicted value of the air conditioning cooling capacity.

[0011] This case designs a thermodynamic model of the distribution station from several important aspects: spatial structure, temporal structure, and grid load demand. It fully considers the impact of the distribution station's spatial size and structure on the transfer of heat energy within the distribution station, while also considering the problem of the distribution station absorbing heat energy based on a time period. It also considers the impact of the grid load on the heat loss of the distribution equipment, thus achieving comprehensive and effective supervision of the impact of heat energy within the distribution station. At the same time, with the development of intelligent power grids, the distribution equipment in the distribution station is also gradually increasing. The design of this case can also take into account the increase in equipment in the distribution station. The spatial structure characteristics of the distribution station are adjusted according to the added distribution equipment, and the surface temperature monitoring of the distribution equipment is increased.

[0012] Specifically, in Step 1, the distribution station spatial structure feature F space It includes the exterior wall orientation, exterior wall volume, interior volume, effective volume and window-to-wall ratio of the distribution station calculated based on the geographical location and structural dimensions of the distribution station. The effective volume is the space after deducting the equivalent volume of all distribution equipment from the interior volume of the distribution station. The window-to-wall ratio is the ratio of the area of ​​the heat dissipation windows on each exterior wall of the distribution station to the area of ​​the exterior wall to which it belongs.

[0013] Specifically, in Step 2, the time feature F time Including quarterly information, weekly information, rest day information and daily scheduling time information, the distribution station temperature characteristic F tem Including distribution equipment surface temperature series, outdoor temperature, and sunshine information.

[0014] Specifically, in Step 3, the spatial fusion module, temporal fusion module, and active demand fusion module in the distribution station thermodynamic model have the same structure, including a connection layer, a 1×1 convolution, a 3×3 convolution, a layer normalization layer, and a Sigmoid activation function. The feature fusion process is expressed as follows:

[0015]

[0016]

[0017]

[0018] Among them: Cat represents the feature connection operation, represents a convolution with a 1×1 kernel, denotes a convolution with a 3×3 convolution kernel, and Norm denotes a layer normalization operation.

[0019] Specifically, in Step 3, the multi-dimensional fusion feature is obtained by the adaptive adjustment fusion module in the distribution station thermodynamic model. The steps include:

[0020] Step 31, first integrate the spatial features Temporal fusion features and active power demand fusion characteristics Add element by element to get Then F1 c Input to the channel attention unit and spatial attention unit at the same time;

[0021] Step 32: In the channel attention unit, first focus on F1 along the channel dimension. c Perform global average pooling operation Then, a 1×1 convolution is used to reduce the channel dimension, a ReLU activation function is used for nonlinear enhancement, and another 1×1 convolution is used to reduce the channel dimension. Expressed as:

[0022]

[0023] Step 33: In the spatial attention unit, first focus on F1 along the spatial dimension. c Perform global average pooling operation and global maximum pooling operation Then connect along the spatial dimension and input it into a 7×7 convolution to get Expressed as:

[0024]

[0025] in: represents a convolution with a 7×7 convolution kernel;

[0026] Step 34, and Add element by element to get Based on F1 c The corresponding channel pair Each channel of F1 is adjusted first along the channel dimension. c and Connect them, then rearrange each channel in an alternating manner through the channel shuffle operation, then pass the group convolution, and finally pass the Sigmoid activation function to obtain the fusion weight map R, which is expressed as:

[0027]

[0028] Among them: CS represents the channel shuffle operation, represents a group convolution with a 7×7 convolution kernel;

[0029] Step 34, map the fusion weight R to the spatial fusion feature Temporal fusion features and active power demand fusion characteristics Perform element-wise multiplication to obtain and

[0030] Step 34: Add each feature element by element, and then perform point-by-point convolution to obtain multi-dimensional fusion features. Expressed as:

[0031]

[0032] in: Represents a point-wise convolution with a 1×1 kernel.

[0033] Specifically, in Step 4, the mean absolute error function MAE and the mean absolute percentage error function MAPE are used as loss functions to supervise the training of the distribution station thermodynamic model:

[0034]

[0035]

[0036] Where: K represents the number of samples in the training data set.

[0037] Specifically, the distribution room air conditioning load regulation method is executed by a distribution room air conditioning load regulation system, which includes a distribution station spatial structure feature extraction module, a data acquisition module, a distribution station thermodynamic model and a supervision training module;

[0038] The distribution station spatial structure feature extraction module first calculates the distribution station's exterior wall orientation, exterior wall volume, interior volume, effective volume, and window-to-wall ratio based on the distribution station's geographical location and structural dimensions, and then extracts the distribution station's spatial structure feature F based on the calculation results. space ;

[0039] The data acquisition module collects the geographical location and structural dimensions of the distribution station and sends them to the distribution station spatial structure feature extraction module; collects quarterly information, weekday information, rest day information and daily scheduling time information, and sends them to the distribution station thermodynamic model; collects the surface temperature series of distribution equipment, outdoor temperature, and sunshine information, and sends them to the distribution station thermodynamic model; collects the net load active power demand of the distribution network and sends it to the distribution station thermodynamic model;

[0040] The distribution station thermodynamic model includes a distribution station temperature feature extraction module, a space fusion module, a time fusion module, an active demand fusion module, an adaptive adjustment fusion module and a detection head; the distribution station temperature feature extraction module is used to extract the distribution station temperature feature F tem The spatial fusion module is used to integrate the spatial structure characteristics of the distribution station F space and the distribution station temperature characteristic F tem Get spatial fusion features The time fusion module is used to extract the time feature F time , and integrate the temporal feature F time and the distribution station temperature characteristic F tem Get time fusion features The active power demand fusion module is used to extract the active power demand characteristics F of the net load of the distribution network. P , and integrate the net load active demand characteristics of the distribution network F P and the distribution station temperature characteristic F tem Get the active power demand fusion feature F P ; The adaptive adjustment fusion module is used to adjust the spatial fusion features Temporal fusion features and active power demand fusion characteristics Perform multi-dimensional fusion to obtain multi-dimensional fusion features The detection head uses multi-dimensional fusion features Perform semantic segmentation to obtain the predicted value Q of air conditioning cooling capacitypre ;

[0041] The supervised training module uses a loss function to perform supervised training on the distribution station thermodynamic model to obtain a trained distribution station thermodynamic model.

[0042] Beneficial effects: The distribution room air-conditioning load regulation method based on model prediction provided by the present invention has the following advantages over the existing technology: 1. The air-conditioning load of the distribution station is modeled based on the artificial intelligence method, and the air-conditioning cooling capacity is predicted, thereby realizing the air-conditioning load regulation of the distribution room, thereby improving the intelligence level of power grid regulation; 2. The distribution station thermodynamic model is trained from several important aspects such as spatial structure, time structure and power grid load demand, and the air-conditioning load regulation of the distribution station can be met without adding other parameters; 3. It can meet the situation of increasing distribution equipment as the intelligence level of the distribution network increases; 4. The influence of different factors on the air-conditioning load is preliminarily adjusted by feature fusion, and the influence of different factors on the air-conditioning load is further adjusted by adaptive adjustment fusion, so that the learning speed of the distribution station thermodynamic model is faster. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the implementation process of the present invention. DETAILED DESCRIPTION

[0044] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] like Figure 1 The figure shows a distribution room air conditioning load regulation method based on model prediction, which is executed by a distribution room air conditioning load regulation system. The distribution room air conditioning load regulation system includes a distribution station spatial structure feature extraction module, a data acquisition module, a distribution station thermodynamic model and a supervised training module.

[0046] The distribution station spatial structure feature extraction module first calculates the distribution station's exterior wall orientation, exterior wall volume, interior volume, effective volume, and window-to-wall ratio based on the distribution station's geographical location and structural dimensions, and then extracts the distribution station's spatial structure feature F based on the calculation results. space .

[0047] The data acquisition module collects the geographical location and structural dimensions of the distribution station and sends them to the distribution station spatial structure feature extraction module; collects quarterly information, weekly information, rest day information and daily scheduling time information, and sends them to the distribution station thermodynamic model; collects the surface temperature sequence of the distribution equipment, outdoor temperature, and sunshine information, and sends them to the distribution station thermodynamic model; collects the net load active power demand of the distribution network, and sends it to the distribution station thermodynamic model.

[0048] The distribution station thermodynamic model includes a distribution station temperature feature extraction module, a space fusion module, a time fusion module, an active demand fusion module, an adaptive adjustment fusion module and a detection head; the distribution station temperature feature extraction module is used to extract the distribution station temperature feature F tem The spatial fusion module is used to integrate the spatial structure characteristics of the distribution station F space and the distribution station temperature characteristic F tem Get spatial fusion features The time fusion module is used to extract the time feature F time , and integrate the temporal feature F time and the distribution station temperature characteristic F tem Get time fusion features The active power demand fusion module is used to extract the active power demand characteristics F of the net load of the distribution network. P , and integrate the net load active demand characteristics of the distribution network F P and the distribution station temperature characteristic F tem Get the active power demand fusion feature F P ; The adaptive adjustment fusion module is used to adjust the spatial fusion features Temporal fusion features and active power demand fusion characteristics Perform multi-dimensional fusion to obtain multi-dimensional fusion features The detection head uses multi-dimensional fusion features Perform semantic segmentation to obtain the predicted value Q of air conditioning cooling capacity pre .

[0049] The supervised training module uses a loss function to perform supervised training on the distribution station thermodynamic model to obtain a trained distribution station thermodynamic model.

[0050] The present invention will be further described below in conjunction with the specific implementation steps of the method of this case.

[0051] Step 1: Construct a distribution station spatial structure feature extraction module

[0052] Construct a distribution station spatial structure feature extraction module to extract the distribution station spatial structure feature F space ; The distribution station spatial structure characteristics F space It includes the exterior wall orientation, exterior wall volume, interior volume, effective volume and window-to-wall ratio of the distribution station calculated based on the geographical location and structural dimensions of the distribution station. The effective volume is the space after deducting the equivalent volume of all distribution equipment from the interior volume of the distribution station. The window-to-wall ratio is the ratio of the area of ​​the heat dissipation windows on each exterior wall of the distribution station to the area of ​​the exterior wall to which it belongs.

[0053] Step 2: Obtain historical data on heat loss and air conditioning load of power distribution equipment

[0054] Obtain historical data on heat loss and air conditioning load of power distribution equipment, including time characteristics F time , distribution network net load active demand characteristics F P , distribution station temperature characteristics F tem and air conditioning cooling capacity label Q ture , the historical data is divided into a training data set and a test data set in proportion; the time feature F time Including quarterly information, weekly information, rest day information and daily scheduling time information, the distribution station temperature characteristic F tem Including distribution equipment surface temperature series, outdoor temperature, and sunshine information.

[0055] Step 3: Construct a thermodynamic model of the distribution station based on a neural network

[0056] The distribution station thermodynamic model is firstly fused with the spatial structure characteristics F of the distribution station through the spatial fusion module. space and the distribution station temperature characteristic F tem Perform feature fusion to obtain spatial fusion features By spatial fusion features Characterize the impact of the spatial structure of the distribution room on the air conditioning load; then use the time fusion module to analyze the time characteristics F time and the distribution station temperature characteristic F tem Perform feature fusion to obtain time fusion features Fusion of features through time Characterize the impact of time information on air conditioning load; then use the active demand fusion module to fusion the active demand characteristics of the distribution network net load F P and the distribution station temperature characteristic F tem Perform feature fusion to obtain active demand fusion features Fusion characteristics through active power demand Characterize the impact of the net load active demand of the distribution network on the air conditioning load; finally, the spatial fusion characteristics are adjusted by adaptively adjusting the fusion module Temporal fusion features and active power demand fusion characteristics Perform multi-dimensional fusion to obtain multi-dimensional fusion features Multidimensional fusion features Input to the detection head for semantic segmentation to obtain the predicted value Q of air conditioning cooling capacity pre .

[0057] The spatial fusion module, temporal fusion module, and active power demand fusion module in the distribution station thermodynamic model have the same structure, which includes a connection layer, a 1×1 convolution, a 3×3 convolution, a layer normalization layer, and a Sigmoid activation function. The feature fusion process is expressed as follows:

[0058]

[0059]

[0060]

[0061] Among them: Cat represents the feature connection operation, represents a convolution with a 1×1 kernel, denotes a convolution with a 3×3 convolution kernel, and Norm denotes a layer normalization operation.

[0062] Obtaining multidimensional fusion features through adaptive adjustment fusion module in distribution station thermodynamic model Here are the steps:

[0063] Step 31, first integrate the spatial features Temporal fusion features and active power demand fusion characteristics Add element by element to get Then F1 c Input to the channel attention unit and spatial attention unit at the same time;

[0064] Step 32: In the channel attention unit, first focus on F1 along the channel dimension. c Perform global average pooling operation Then, a 1×1 convolution is used to reduce the channel dimension, a ReLU activation function is used for nonlinear enhancement, and another 1×1 convolution is used to reduce the channel dimension. Expressed as:

[0065]

[0066] Step 33: In the spatial attention unit, first focus on F1 along the spatial dimension. c Perform global average pooling operation and global maximum pooling operation Then connect along the spatial dimension and input it into a 7×7 convolution to get Expressed as:

[0067]

[0068] in: represents a convolution with a 7×7 convolution kernel;

[0069] Step 34, and Add element by element to get Based on F1 c The corresponding channel pair Each channel of F1 is adjusted first along the channel dimension.c and Connect them, then rearrange each channel in an alternating manner through the channel shuffle operation, then pass the group convolution, and finally pass the Sigmoid activation function to obtain the fusion weight map R, which is expressed as:

[0070]

[0071] Among them: CS represents the channel shuffle operation, represents a group convolution with a 7×7 convolution kernel;

[0072] Step 34, map the fusion weight R to the spatial fusion feature Temporal fusion features and active power demand fusion characteristics Perform element-wise multiplication to obtain and

[0073] Step 34: Add each feature element by element, and then perform point-by-point convolution to obtain multi-dimensional fusion features. Expressed as:

[0074]

[0075] in: Represents a point-wise convolution with a 1×1 kernel.

[0076] Step 4: Use loss function to supervise the training of the distribution station thermodynamic model

[0077] The mean absolute error function MAE and the mean absolute percentage error function MAPE are used as loss functions to supervise the training of the distribution station thermodynamic model, and then the trained distribution station thermodynamic model is obtained:

[0078]

[0079]

[0080] Where: K represents the number of samples in the training data set.

[0081] Step 5: Adjust the air conditioning load in the distribution room based on the predicted value of air conditioning cooling capacity

[0082] The spatial structure characteristics of the distribution station and the test data set are input into the trained distribution station thermodynamic model to obtain the predicted value of air conditioning cooling capacity, and the air conditioning load of the distribution room is adjusted based on the predicted value of air conditioning cooling capacity.

[0083] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the above embodiments do not limit the present invention in any form, and any technical solutions obtained by equivalent replacement or equivalent transformation fall within the scope of protection of the present invention.

Claims

1. A method for regulating air conditioning load in a power distribution room based on model prediction, characterized in that: The steps include: Step 1: Construct a distribution station spatial structure feature extraction module to extract the distribution station spatial structure feature F space ; Step 2: Obtain historical data on heat loss and air conditioning load of power distribution equipment, including time characteristics F time , distribution network net load active demand characteristics F P , distribution station temperature characteristics F tem and air conditioning cooling capacity label Q ture , historical data is divided into training data set and test data set in proportion; Step 3: Construct a thermodynamic model of the distribution station based on the neural network; first, use the spatial fusion module to analyze the spatial structure characteristics F of the distribution station. space and the distribution station temperature characteristic F tem Perform feature fusion to obtain spatial fusion features By spatial fusion features Characterize the impact of the spatial structure of the distribution room on the air conditioning load; then use the time fusion module to analyze the time characteristics F time and the distribution station temperature characteristic F tem Perform feature fusion to obtain time fusion features Fusion of features through time Characterize the impact of time information on air conditioning load; then use the active demand fusion module to fusion the active demand characteristics of the distribution network net load F P and the distribution station temperature characteristic F tem Perform feature fusion to obtain active demand fusion features Fusion characteristics through active power demand Characterize the impact of the net load active demand of the distribution network on the air conditioning load; finally, the spatial fusion characteristics are adjusted by adaptively adjusting the fusion module Temporal fusion features and active power demand fusion characteristics Perform multi-dimensional fusion to obtain multi-dimensional fusion features Multidimensional fusion features Input to the detection head for semantic segmentation to obtain the predicted value Q of air conditioning cooling capacity pre ; Step 4: Use the loss function to supervise the training of the distribution station thermodynamic model to obtain a trained distribution station thermodynamic model; Step 5: Input the spatial structure characteristics of the distribution station and the test data set into the trained distribution station thermodynamic model to obtain the predicted value of the air conditioning cooling capacity, and adjust the air conditioning load of the distribution room based on the predicted value of the air conditioning cooling capacity.

2. The method for regulating the air-conditioning load in a power distribution room based on model prediction according to claim 1, characterized in that: In Step 1, the spatial structure characteristics of the distribution station F space It includes the exterior wall orientation, exterior wall volume, interior volume, effective volume and window-to-wall ratio of the distribution station calculated based on the geographical location and structural dimensions of the distribution station. The effective volume is the space after deducting the equivalent volume of all distribution equipment from the interior volume of the distribution station. The window-to-wall ratio is the ratio of the area of ​​the heat dissipation windows on each exterior wall of the distribution station to the area of ​​the exterior wall to which it belongs.

3. The method for regulating air conditioning load in a power distribution room based on model prediction according to claim 1, characterized in that: In Step 2, the time feature F time Including quarterly information, weekly information, rest day information and daily scheduling time information, the distribution station temperature characteristic F tem Including distribution equipment surface temperature series, outdoor temperature, and sunshine information.

4. The method for regulating air conditioning load in a power distribution room based on model prediction according to claim 1, characterized in that: In Step 3, the spatial fusion module, temporal fusion module, and active power demand fusion module in the distribution station thermodynamic model have the same structure, including a connection layer, a 1×1 convolution, a 3×3 convolution, a layer normalization layer, and a Sigmoid activation function. The feature fusion process is expressed as follows: Among them: Cat represents the feature connection operation, represents a convolution with a 1×1 kernel, denotes a convolution with a 3×3 convolution kernel, and Norm denotes a layer normalization operation.

5. The method for regulating air conditioning load in a power distribution room based on model prediction according to claim 1, characterized in that: In Step 3, the multi-dimensional fusion features are obtained by adaptively adjusting the fusion module in the distribution station thermodynamic model. The steps include: Step 31, first integrate the spatial features Temporal fusion features and active power demand fusion characteristics Add element by element to get Then F1 c Input to the channel attention unit and spatial attention unit at the same time; Step 32: In the channel attention unit, first focus on F1 along the channel dimension. c Perform global average pooling operation Then, a 1×1 convolution is used to reduce the channel dimension, a ReLU activation function is used for nonlinear enhancement, and another 1×1 convolution is used to reduce the channel dimension. Expressed as: Step 33: In the spatial attention unit, first focus on F1 along the spatial dimension. c Perform global average pooling operation and global maximum pooling operation Then connect along the spatial dimension and input it into a 7×7 convolution to get F3 c , expressed as: in: represents a convolution with a 7×7 convolution kernel; Step 34, and F3 c Add element by element to get Based on F1 c The corresponding channel pair Each channel of F1 is adjusted first along the channel dimension. c and Connect them, then rearrange each channel in an alternating manner through the channel shuffle operation, then pass the group convolution, and finally pass the Sigmoid activation function to obtain the fusion weight map R, which is expressed as: Among them: CS represents the channel shuffle operation, represents a group convolution with a 7×7 convolution kernel; Step 34, map the fusion weight R to the spatial fusion feature Temporal fusion features and active power demand fusion characteristics Perform element-wise multiplication to obtain and Step 34: Add each feature element by element, and then perform point-by-point convolution to obtain multi-dimensional fusion features. Expressed as: in: Represents a point-wise convolution with a 1×1 kernel.

6. The method for regulating air conditioning load in a power distribution room based on model prediction according to claim 1, characterized in that: In Step 4, the mean absolute error function MAE and the mean absolute percentage error function MAPE are used as loss functions to supervise the training of the distribution station thermodynamic model: Where: K represents the number of samples in the training data set.

7. The method for regulating air conditioning load in a power distribution room based on model prediction according to claim 1, characterized in that: The power distribution room air conditioning load regulation method is executed by a power distribution room air conditioning load regulation system, wherein the power distribution room air conditioning load regulation system includes a power distribution station spatial structure feature extraction module, a data acquisition module, a power distribution station thermodynamic model and a supervision training module; The distribution station spatial structure feature extraction module first calculates the distribution station's exterior wall orientation, exterior wall volume, interior volume, effective volume, and window-to-wall ratio based on the distribution station's geographical location and structural dimensions, and then extracts the distribution station's spatial structure feature F based on the calculation results. space ; The data acquisition module collects the geographical location and structural dimensions of the distribution station and sends them to the distribution station spatial structure feature extraction module; Collect quarterly information, weekly information, rest day information, and daily scheduling time information, and send it to the distribution station thermodynamic model; collect distribution equipment surface temperature series, outdoor temperature, and sunshine information, and send it to the distribution station thermodynamic model; collect the net load active power demand of the distribution network, and send it to the distribution station thermodynamic model; The distribution station thermodynamic model includes a distribution station temperature feature extraction module, a spatial fusion module, a temporal fusion module, an active power demand fusion module, an adaptive adjustment fusion module and a detection head; The distribution station temperature feature extraction module is used to extract the distribution station temperature feature F tem The spatial fusion module is used to integrate the spatial structure characteristics of the distribution station F space and the distribution station temperature characteristic F tem Get spatial fusion features The time fusion module is used to extract the time feature F time , and integrate the temporal feature F time and the distribution station temperature characteristic F tem Get time fusion features The active power demand fusion module is used to extract the active power demand characteristics F of the net load of the distribution network. P , and integrate the net load active demand characteristics of the distribution network F P and the distribution station temperature characteristic F tem Get the active power demand fusion feature F P ; The adaptive adjustment fusion module is used to adjust the spatial fusion features Temporal fusion features and active power demand fusion characteristics Perform multi-dimensional fusion to obtain multi-dimensional fusion features The detection head uses multi-dimensional fusion features Perform semantic segmentation to obtain the predicted value Q of air conditioning cooling capacity pre ; The supervised training module uses a loss function to perform supervised training on the distribution station thermodynamic model to obtain a trained distribution station thermodynamic model.

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