Prediction method and device for weather-climate integrated intelligent large model

Abstract

The invention relates to the technical field of mathematical models, and discloses a prediction method and device for a weather-climate integrated intelligent large model. The method comprises the following steps: fusing weather information of a prediction time period and climate information of the prediction time period to obtain weather-climate characteristics; calculating original weather-climate change trend characteristics according to the weather information and the climate information of the historical time period; inputting the original weather-climate change trend characteristics and the power consumption change trend characteristics into an attention mechanism model to obtain target weather-climate change trend characteristics conforming to the power consumption change trend; splicing the weather-climate characteristics with the target weather-climate change trend characteristics to generate spliced characteristics; and forming a feature matrix by the spliced features, the equipment information and the population information, and inputting the feature matrix into a prediction model to obtain the power consumption of the cold supply system in a prediction period. According to the invention, accurate prediction of the power consumption of the cooling system in a regional concentrated cooling scene is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of mathematical models, and in particular to a prediction method and device for a weather-climate integrated intelligent large model. Background Art

[0002] Centralized cooling has the characteristics of intensive land use, peak load reduction, high efficiency and energy saving, intelligent management and control, and avoidance of repeated investment. It is suitable for promotion and application in areas with high density of commercial buildings such as urban CBD. Users of regional centralized cooling can flexibly adjust temperature and humidity according to actual use needs to ensure the stability of indoor temperature and humidity.

[0003] The cooling of the regional centralized cooling system relies on water. The regional centralized cooling system is usually composed of energy stations (cold and heat sources), transmission pipelines, cold (heat) exchange stations and cold terminals (such as fan coil units). The regional cooling pipelines between energy stations and building complexes are mostly used to transmit cold water. The entire cooling system is similar to the human heart and blood circulation system. Compared with cooling for each user separately, regional centralized cooling can significantly reduce electricity consumption.

[0004] In the prior art, the power consumption of the centralized cooling system is generally determined based on the predicted weather temperature. This prediction method is quite different from the actual power consumption, resulting in incorrect power consumption prediction. In view of this, the present invention is proposed. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a prediction method and device for a weather-climate integrated intelligent large model, which realizes the accurate prediction of the power consumption of the cooling system in the scenario of regional centralized cooling.

[0006] In a first aspect, an embodiment of the present invention provides a prediction method for a weather-climate integrated intelligent large model, comprising:

[0007] Collecting equipment information of the cooling system during the forecast period, population information in the region during the forecast period, weather information and climate information during the forecast period and historical periods, and power consumption of the cooling system during historical periods; the historical periods are adjacent to the forecast period;

[0008] The weather information of the forecast period and the climate information of the forecast period are integrated to obtain the weather-climate characteristics;

[0009] Calculate the power consumption trend characteristics based on the power consumption of the cooling system during historical periods;

[0010] Calculate the original weather-climate change trend characteristics based on the weather information and climate information of the historical period; input the original weather-climate change trend characteristics and the power consumption change trend characteristics into the attention mechanism model to obtain the target weather-climate change trend characteristics that conform to the power consumption change trend;

[0011] splicing the weather-climate feature with the target weather-climate change trend feature to generate a spliced ​​feature;

[0012] The spliced ​​features, the equipment information and the population information are combined into a feature matrix, which is input into a prediction model to obtain the power consumption of the cooling system during the prediction period; the prediction model is a deep neural network model.

[0013] Optionally, the population information collected in the region during the forecast period includes:

[0014] Collect the population inflow and outflow and the total permanent population in the area under the same historical weather and climate;

[0015] Based on the inflow and outflow of the population and the total resident population, determine the proportion of the population in the region to the total resident population during the same historical period;

[0016] Based on the total permanent population during the forecast period and the said ratio, the population size in the area during the forecast period is determined.

[0017] Optionally, the calculation of original weather-climate change trend characteristics based on weather information and climate information in the historical period includes:

[0018] Calculate the weather change trend based on the weather information of adjacent periods in the historical period, and calculate the climate change trend based on the climate information of adjacent periods in the historical period;

[0019] The changing trends of weather and climate are integrated to obtain the original weather-climate change trend characteristics.

[0020] Optionally, the weather information includes temperature, humidity, wind force and wind direction; and the climate information includes sunlight and precipitation.

[0021] Optionally, the step of inputting the weather-climate characteristics, the target weather-climate change trend characteristics, the equipment information and the population information into a prediction model to obtain the power consumption of the cooling system during the prediction period includes:

[0022] splicing the weather-climate feature with the target weather-climate change trend feature to generate a spliced ​​feature;

[0023] The spliced ​​features, the equipment information and the population information are combined into a feature matrix, which is input into a prediction model to obtain the power consumption of the cooling system during the prediction period.

[0024] Optionally, the weather information and the climate information of the forecast period are integrated to obtain weather-climate characteristics, including:

[0025] Normalize the weather information of the forecast period to generate the first column vector;

[0026] Normalize the climate information at the prediction time to generate the second column vector;

[0027] Performing weighted summation of the first column vector and the second column vector to obtain weather-climate characteristics;

[0028] Among them, the weights of the first column vector and the second column vector are trained together with the attention mechanism model and the prediction model.

[0029] In a second aspect, an embodiment of the present invention provides a prediction device for a weather-climate integrated intelligent large model, comprising:

[0030] A collection module, used to collect equipment information of the cooling system in the forecast period, population information in the region in the forecast period, weather information and climate information in the forecast period and historical period, and power consumption of the cooling system in the historical period; the historical period is adjacent to the forecast period;

[0031] A fusion module is used to fuse the weather information of the forecast period with the climate information of the forecast period to obtain weather-climate characteristics;

[0032] A calculation module, used to calculate the power consumption change trend characteristics according to the power consumption of the cooling system in historical periods;

[0033] A mapping module is used to calculate the original weather-climate change trend characteristics according to the weather information and climate information of the historical period; input the original weather-climate change trend characteristics and the power consumption change trend characteristics into the attention mechanism model to obtain the target weather-climate change trend characteristics that conform to the power consumption change trend;

[0034] A prediction module, used for splicing the weather-climate characteristics with target weather-climate change trend characteristics to generate spliced ​​characteristics;

[0035] The spliced ​​features, the equipment information and the population information are combined into a feature matrix, which is input into a prediction model to obtain the power consumption of the cooling system during the prediction period; the prediction model is a deep neural network model.

[0036] The embodiments of the present invention have the following technical effects:

[0037] 1. The present invention generally adopts the fusion of weather and climate information to predict the power consumption of the cooling system, which is more accurate than predicting based on weather temperature alone.

[0038] 2. The present invention takes into account the equipment information of the cooling system and the difference in power consumption caused by different equipment models / types.

[0039] 3. The present invention inputs the original weather-climate change trend characteristics and the power consumption change trend characteristics into the attention mechanism model to obtain the target weather-climate change trend characteristics that conform to the power consumption change trend. Through the calculation of the change trend and the introduction of the attention mechanism model, the target weather-climate change trend characteristics describe how the change trend affects the power consumption, that is, the impact characteristics of weather-climate on power consumption are extracted. This impact characteristic, as an input feature of the prediction model, can significantly improve the accuracy of the power consumption prediction value.

[0040] 4. The present invention uses large model technology through feature collection, fusion, attention mapping and prediction to finally obtain the power consumption of the cooling system in the prediction period. The combined large model has stronger learning and generalization capabilities. With the increase of training samples, the overall prediction accuracy of the large model will become higher and higher. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 It is a flow chart of a prediction method of a weather-climate integrated intelligent large model provided by an embodiment of the present invention;

[0043] Figure 2 It is a structural schematic diagram of a weather-climate integrated intelligent large model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0045] As mentioned in the background technology, in the prior art, the power consumption of the centralized cooling system is generally determined based on the predicted weather temperature, and the actual power consumption varies greatly. This is because the power consumption of the cooling system is affected not only by weather temperature, but also by other factors. When there are multi-dimensional influencing factors, how to deal with these factors and how to build a large model require creative work.

[0046] The method provided by the present invention is applicable to the prediction of power consumption of regional cooling systems. Regional cooling is to set up a centralized refrigeration station in a building complex to prepare air-conditioning chilled water, and then provide air-conditioning cooling to each building through a circulating water pipeline system, such as buildings and communities. The cooling water pump, control device, etc. in the cooling system all consume electricity, and the power consumption is positively correlated with the cooling capacity. The method provided by the present invention is executed by a prediction device of a weather-climate integrated intelligent large model, which is integrated in an electronic device in the form of hardware and / or software.

[0047] Embodiment 1

[0048] Figure 1 is a flow chart of a prediction method of a weather-climate integrated intelligent large model provided by an embodiment of the present invention, Figure 2 It is a structural schematic diagram of a weather-climate integrated intelligent large model provided by an embodiment of the present invention.

[0049] See also Figure 1 and Figure 2 , the method provided by the embodiment of the present invention includes the following operations:

[0050] S110, collecting equipment information of the cooling system in the forecast period, population information in the region in the forecast period, weather information and climate information in the forecast period and historical periods, and power consumption of the cooling system in historical periods.

[0051] The forecast period refers to tomorrow or the next few hours. The historical period refers to the period adjacent to the forecast period, such as yesterday or the previous few hours.

[0052] The equipment information of the cooling system includes the power and efficiency of the electrical equipment, which directly affects the power consumption, such as the power and efficiency of the chiller, and the power and efficiency of the water pump.

[0053] The population information in the region mainly includes the population size. In practical applications, considering that people of different ages and genders have different demands for cooling, it may also include the gender distribution of the population and the age distribution of the population. The population information in the region can be obtained through investigation. Preferably, considering that population mobility is affected by weather and climate, for example, when it is hot or rainy, a large number of people will gather in the region and will not go out; for example, when the weather is relatively cool, some people will go out to play. In order to accurately reflect the impact of weather and climate on population size, this embodiment collects population information in the region during the forecast period, including: collecting the inflow and outflow of the population and the total number of permanent residents in the region during the same historical weather and climate; determining the proportion of the population in the region to the total number of permanent residents during the same historical weather and climate based on the inflow and outflow of the population and the total number of permanent residents; determining the population size in the region during the forecast period based on the total number of permanent residents during the forecast period and the proportion.

[0054] For example, compared with the weather and climate in the forecast period, select period A with the same weather and climate in the historical period, and count the in and out volume and the total permanent population B through the camera installed on the porch or the entry and exit pass in period A. Subtract the outflow volume from the total permanent population, and add the inflow volume to get the population C in the region during the same historical weather and climate. C / B gets the ratio of the population in the region to the total permanent population, that is, approximately this proportion of the population stays in the region under this weather and climate. If the total permanent population in the forecast period is D, then D is multiplied by the ratio to get the population in the forecast period. Since the forecast period is tomorrow or the next few hours, the total permanent population of the current period can be used as the total permanent population of the forecast period.

[0055] The weather information and climate information for the forecast period can be obtained from the meteorological department. The weather information includes temperature, humidity, wind speed and wind direction; the climate information includes sunlight and precipitation.

[0056] The power consumption of the cooling system during the historical period can be read through the power meter of the cooling system.

[0057] S120: Fusing the weather information of the forecast period and the climate information of the forecast period to obtain weather-climate characteristics.

[0058] Weather and climate coexist in time and space, and together bring people a sense of cold and heat. This embodiment explores the impact of the integration of weather and climate on human perception, which can better reflect people's demand for cooling, thereby directly affecting the power consumption of the cooling system. Specifically, the weather information of the forecast period is normalized to generate a first column vector; the climate information of the forecast time is normalized to generate a second column vector; the first column vector and the second column vector are weighted and summed to obtain the weather-climate characteristics.

[0059] The purpose of normalization is to convert the temperature, humidity, wind speed and direction in weather information, and the light and precipitation in climate information to a unified range for feature calculation. Before normalization, the weather information and climate information need to be feature encoded, using numbers to represent specific information.

[0060] The weights of the first and second column vectors represent the impact on the power consumption of the cooling system. The weights can be pre-set to an initial value and then updated during the training process of the large model.

[0061] S130. Calculate the power consumption change trend characteristics based on the power consumption of the cooling system during the historical period.

[0062] For example, if it is May 21st, the historical period includes May 18th, May 19th and May 20th. The power consumption of the cooling system every day in history can be obtained through the electric meter. The difference in power consumption between two consecutive days is calculated, that is, the difference in power consumption between May 18th and May 19th, and the difference in power consumption between May 19th and May 20th, and several difference values ​​are obtained as the power consumption change trend characteristics.

[0063] S140. Calculate original weather-climate change trend characteristics based on weather information and climate information in historical periods; input the original weather-climate change trend characteristics and power consumption change trend characteristics into the attention mechanism model to obtain target weather-climate change trend characteristics that conform to the power consumption change trend.

[0064] The weather change trend is calculated based on the weather information of adjacent periods in the historical period, and the climate change trend is calculated based on the climate information of adjacent periods in the historical period. Specifically, similar to the power consumption change trend feature, the feature codes of the weather information of two adjacent days are subtracted to obtain a number of differences as the weather change trend; the feature codes of the climate information of two adjacent days are subtracted to obtain a number of differences as the climate change trend.

[0065] Then, the weather change trend and the climate change trend are fused to obtain the original weather-climate change trend feature. The fusion method can be to splice the weather change trend and the climate change trend into a string as the original weather-climate change trend feature.

[0066] The original weather-climate change trend features and the power consumption change trend features obtained in S130 are input into the attention mechanism model self-attention to obtain the target weather-climate change trend features that conform to the power consumption change trend output by self-attention. Target weather-climate change trend features. The self-attention model selectively filters out a small amount of important information from a large amount of information and focuses on these important information, ignoring most of the unimportant information. In this embodiment, the self-attention model obtains weather-climate change trend features that have a significant impact on the change in power consumption from the original weather-climate change trend features. The present invention uses "change trend" as the input feature of the self-attention model. This is because when the weather and climate change, the power consumption must follow the change. Only in this dynamic change trend can the "weather-climate features" that have a significant impact on the power consumption be found.

[0067] S150. Splice the weather-climate characteristics with the target weather-climate change trend characteristics to generate spliced ​​characteristics; combine the spliced ​​characteristics with the equipment information and the population information to form a feature matrix, and input it into a prediction model to obtain the power consumption of the cooling system during the prediction period; the prediction model is a deep neural network model.

[0068] The weather-climate characteristics in S150 are obtained in S120 and reflect the characteristics of the forecast period. The target weather-climate change trend characteristics are obtained according to S140 and reflect the characteristics of the historical period. The device information and the population information are for the forecast period and reflect the impact on power consumption from the physical level and social level respectively.

[0069] The present invention introduces the features of the above-mentioned various aspects and inputs them into the prediction model for prediction, which is conducive to improving the accuracy of the prediction.

[0070] Optionally, the prediction model is a deep neural network model, such as a BP neural network model or an LSTM model.

[0071] The weather-climate characteristics (dimension 1×q) are concatenated with the target weather-climate change trend characteristics (dimension 1×n) to generate concatenated characteristics (dimension 2×max(n,q)), and the missing parts of the concatenated characteristics are filled with 0. The concatenated characteristics are combined with the equipment information (dimension 1×m) and the population information (1×p) to form a feature matrix (4×max(n,m,q,p)). The missing parts of the feature matrix are filled with 0. The feature matrix is ​​input into the prediction model to obtain the power consumption of the cooling system during the prediction period.

[0072] It should be noted that in the embodiment of the present invention, the weights of the first column vector and the second column vector are trained together with the attention mechanism model and the prediction model. During the training process, a sufficient number of samples need to be collected and labeled with labels of the power consumption of the cooling system. The samples include: equipment information of the cooling system in the first time period, population information in the area in the first time period, weather information and climate information in the first time period and the second time period, and the power consumption of the cooling system in the second time period; the second time period is adjacent to the first time period; the second time period is earlier than the first time period;

[0073] The weather information of the first time period and the climate information of the first time period are integrated to obtain weather-climate characteristics; the power consumption change trend characteristics are calculated according to the power consumption of the cooling system in the second time period; the original weather-climate change trend characteristics are calculated according to the weather information and climate information of the second time period; the original weather-climate change trend characteristics and the power consumption change trend characteristics are input into the attention mechanism model to obtain the target weather-climate change trend characteristics that conform to the power consumption change trend; the weather-climate characteristics, the target weather-climate change trend characteristics, the equipment information and the population information are input into the prediction model to be trained to obtain the power consumption of the cooling system in the prediction period, and the loss function is constructed according to the difference between the power consumption and the label, and the weights of the first column vector and the second column vector, the parameters in the attention mechanism model and the parameters in the prediction model are updated.

[0074] The embodiments of the present invention have the following technical effects:

[0075] 1. The present invention generally adopts the fusion of weather and climate information to predict the power consumption of the cooling system, which is more accurate than predicting based on weather temperature alone.

[0076] 2. The present invention takes into account the equipment information of the cooling system and the difference in power consumption caused by different equipment models / types.

[0077] 3. The present invention inputs the original weather-climate change trend characteristics and the power consumption change trend characteristics into the attention mechanism model to obtain the target weather-climate change trend characteristics that conform to the power consumption change trend. Through the calculation of the change trend and the introduction of the attention mechanism model, the target weather-climate change trend characteristics describe how the change trend affects the power consumption, that is, the impact characteristics of weather-climate on power consumption are extracted. This impact characteristic, as an input feature of the prediction model, can significantly improve the accuracy of the power consumption prediction value.

[0078] 4. The present invention uses large model technology through feature collection, fusion, attention mapping and prediction to finally obtain the power consumption of the cooling system in the prediction period. The combined large model has stronger learning and generalization capabilities. With the increase of training samples, the overall prediction accuracy of the large model will become higher and higher.

[0079] The embodiment of the present invention also provides a prediction device for a weather-climate integrated intelligent large model, comprising:

[0080] A collection module, used to collect equipment information of the cooling system in the forecast period, population information in the region in the forecast period, weather information and climate information in the forecast period and historical period, and power consumption of the cooling system in the historical period; the historical period is adjacent to the forecast period;

[0081] A fusion module is used to fuse the weather information of the forecast period with the climate information of the forecast period to obtain weather-climate characteristics;

[0082] A calculation module, used to calculate the power consumption change trend characteristics according to the power consumption of the cooling system in historical periods;

[0083] A mapping module is used to calculate the original weather-climate change trend characteristics according to the weather information and climate information of the historical period; input the original weather-climate change trend characteristics and the power consumption change trend characteristics into the attention mechanism model to obtain the target weather-climate change trend characteristics that conform to the power consumption change trend;

[0084] The prediction module is used to splice the weather-climate characteristics with the target weather-climate change trend characteristics to generate spliced ​​characteristics; the spliced ​​characteristics are combined with the equipment information and the population information to form a feature matrix, which is input into the prediction model to obtain the power consumption of the cooling system during the prediction period; the prediction model is a deep neural network model.

[0085] The device provided by the present invention is used to execute the prediction method of the weather-climate integrated intelligent large model provided in the above-mentioned embodiment, and has specific corresponding technical effects.

[0086] It should be noted that the terms used in the present invention are only for describing specific embodiments, rather than limiting the scope of the present application. As shown in the present specification, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, the elements defined by the sentence "include one..." do not exclude the presence of other identical elements in the process, method or device including the elements.

[0087] It should also be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", etc. should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A prediction method for a weather-climate integrated intelligent large model, characterized in that: include: Collect equipment information of the cooling system during the forecast period, population information in the region during the forecast period, weather information and climate information during the forecast period and historical periods, and power consumption of the cooling system during historical periods; The historical period is adjacent to the forecast period; The weather information of the forecast period and the climate information of the forecast period are integrated to obtain the weather-climate characteristics; Calculate the power consumption trend characteristics based on the power consumption of the cooling system during historical periods; Calculate the original weather-climate change trend characteristics based on the weather and climate information of the historical period; Inputting the original weather-climate change trend characteristics and the power consumption change trend characteristics into the attention mechanism model to obtain the target weather-climate change trend characteristics that conform to the power consumption change trend; splicing the weather-climate feature with the target weather-climate change trend feature to generate a spliced ​​feature; The spliced ​​features, the equipment information and the population information are combined into a feature matrix, which is input into a prediction model to obtain the power consumption of the cooling system during the prediction period; the prediction model is a deep neural network model.

2. The method according to claim 1, characterized in that The population information collected in the region during the forecast period includes: Collect the population inflow and outflow and the total permanent population in the area under the same historical weather and climate; Based on the inflow and outflow of the population and the total resident population, determine the proportion of the population in the region to the total resident population during the same historical period; Based on the total permanent population during the forecast period and the said ratio, the population size in the area during the forecast period is determined.

3. The method according to claim 2, characterized in that The calculation of the original weather-climate change trend characteristics based on the weather information and climate information of the historical period includes: Calculate the weather change trend based on the weather information of adjacent periods in the historical period, and calculate the climate change trend based on the climate information of adjacent periods in the historical period; The changing trends of weather and climate are integrated to obtain the original weather-climate change trend characteristics.

4. The method according to claim 3, characterized in that The weather information includes temperature, humidity, wind speed and wind direction; the climate information includes sunlight and precipitation.

5. The method according to claim 1, characterized in that The weather information of the forecast period and the climate information of the forecast period are integrated to obtain weather-climate characteristics, including: Normalize the weather information of the forecast period to generate the first column vector; Normalize the climate information at the prediction time to generate the second column vector; Performing weighted summation of the first column vector and the second column vector to obtain weather-climate characteristics; Among them, the weights of the first column vector and the second column vector are trained together with the attention mechanism model and the prediction model.

6. A prediction device for a weather-climate integrated intelligent large model, characterized in that: include: A collection module, used to collect equipment information of the cooling system during the forecast period, population information in the region during the forecast period, weather information and climate information during the forecast period and historical periods, and power consumption of the cooling system during historical periods; The historical period is adjacent to the forecast period; A fusion module is used to fuse the weather information of the forecast period with the climate information of the forecast period to obtain weather-climate characteristics; A calculation module, used to calculate the power consumption change trend characteristics according to the power consumption of the cooling system in historical periods; A mapping module is used to calculate the original weather-climate change trend characteristics based on the weather information and climate information of the historical period; Inputting the original weather-climate change trend characteristics and the power consumption change trend characteristics into the attention mechanism model to obtain the target weather-climate change trend characteristics that conform to the power consumption change trend; A prediction module, used for splicing the weather-climate characteristics with target weather-climate change trend characteristics to generate spliced ​​characteristics; The spliced ​​features, the equipment information and the population information are combined into a feature matrix, which is input into a prediction model to obtain the power consumption of the cooling system during the prediction period; the prediction model is a deep neural network model.

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