Building component energy station management method and system fusing new energy photovoltaic

Abstract

The application provides a building component energy station management method and system fusing photovoltaic new energy, and relates to the technical field of energy management. The method comprises the following steps: obtaining preset time zone meteorological information of a preset area; performing photovoltaic component energy storage analysis on a first building component energy station to obtain a first energy station predicted energy storage amount; obtaining an Nth energy station predicted energy storage amount; obtaining a first building component energy station predicted power consumption amount to an Nth building component energy station predicted power consumption amount; obtaining a first energy station redundant power amount to an Nth energy station redundant power amount; obtaining an electric vehicle demand power supply amount; performing power supply optimization on the redundant power amount to obtain a building component energy station redundant power supply scheme, and performing building component energy station management. The application can solve the problem of insufficient energy supply and demand management refinement, low energy utilization rate and serious waste, improve the refinement of energy management, and reduce energy waste.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy management, and particularly relates to a building component energy station management method and system fusing photovoltaic new energy. BACKGROUND

[0002] The photovoltaic energy station is a power generation facility that directly converts sunlight into electrical energy using solar cells. Specifically, based on the P-N junction photovoltaic effect principle of semiconductor electronic devices, it effectively absorbs solar radiation energy and converts it into electrical energy through conversion devices. In the current technical background, the installed capacity and grid-connected scale of photovoltaic power stations continue to grow rapidly. As an important part of renewable energy, the efficiency and stability of photovoltaic energy stations are crucial to energy supply.

[0003] In the prior art, the energy storage and power consumption prediction of photovoltaic energy stations usually relies on simplified models or historical data, which may not accurately reflect the changes in actual meteorological conditions and energy demand. Therefore, the management and optimized operation of energy stations often lack detailed data support, resulting in low energy utilization efficiency and energy waste. At the same time, as a new type of transportation tool, electric vehicles have higher requirements for the stability and reliability of energy supply, and existing energy management solutions often cannot meet this demand. In addition, existing energy management solutions often lack effective use of redundant power, leading to energy waste and increased operating costs. Therefore, in order to solve these problems, a technical solution is needed that can accurately predict energy storage and power consumption, optimize energy supply and demand balance, and improve energy utilization efficiency.

[0004] In summary, the existing building component energy station management has the problem of insufficient energy supply and demand management refinement, leading to low energy utilization rate and serious waste. SUMMARY

[0005] The purpose of the present application is to provide a building component energy station management method and system fusing photovoltaic new energy, to solve the technical problem in the prior art that when redundant power is used to power electric vehicles, it cannot be balanced based on accurate energy storage prediction and power demand, resulting in low refinement of energy management, further affecting the efficient use of energy and the stable use of electricity by electric vehicles, and ultimately leading to energy waste.

[0006] In view of the above problems, the present application provides a building component energy station management method and system fusing photovoltaic new energy.

[0007] In a first aspect, the present application provides a building component energy station management method integrating photovoltaic new energy, which is realized by a building component energy station management system integrating photovoltaic new energy. The building component energy station management method integrating photovoltaic new energy comprises the following steps: obtaining preset time zone meteorological information of a preset area; performing photovoltaic component energy storage analysis on a first building component energy station according to the preset time zone meteorological information to obtain a first energy station predicted energy storage amount; until performing photovoltaic component energy storage analysis on an Nth building component energy station according to the preset time zone meteorological information to obtain an Nth energy station predicted energy storage amount; performing preset time zone power consumption analysis on the first building component energy station to the Nth building component energy station to obtain a first building component energy station predicted power consumption amount to an Nth building component energy station predicted power consumption amount; performing redundancy power analysis on the first energy station predicted energy storage amount to the Nth energy station predicted energy storage amount according to the first building component energy station predicted power consumption amount to the Nth building component energy station predicted power consumption amount to obtain a first energy station redundancy power amount to an Nth energy station redundancy power amount; performing electric vehicle demand analysis on the preset area to obtain an electric vehicle demand power supply amount; performing power supply optimization on the first energy station redundancy power amount to the Nth energy station redundancy power amount according to the electric vehicle demand power supply amount to obtain a building component energy station redundancy power supply scheme, and performing building component energy station management.

[0008] In a second aspect, the application further provides a building component energy station management system for integrating photovoltaic new energy, which is used to execute the building component energy station management method for integrating photovoltaic new energy as described in the first aspect. The building component energy station management system for integrating photovoltaic new energy comprises: a meteorological information acquisition module, which is used to obtain meteorological information of a preset time zone in a preset area; a first energy storage amount prediction module, which is used to perform photovoltaic component energy storage analysis on a first building component energy station according to the meteorological information of the preset time zone, and obtain a first energy station predicted energy storage amount; an Nth energy storage amount prediction module, which is used to perform photovoltaic component energy storage analysis on an Nth building component energy station according to the meteorological information of the preset time zone, and obtain an Nth energy station predicted energy storage amount; an electricity consumption amount analysis module, which is used to perform preset time zone electricity consumption amount analysis on the first building component energy station to the Nth building component energy station, and obtain a first building component energy station predicted electricity consumption amount to an Nth building component energy station predicted electricity consumption amount; a redundant electricity amount analysis module, which is used to perform redundant electricity amount analysis on the first energy station predicted energy storage amount to the Nth energy station predicted energy storage amount according to the first building component energy station predicted electricity consumption amount to the Nth building component energy station predicted electricity consumption amount, and obtain a first energy station redundant electricity amount to an Nth energy station redundant electricity amount; a demand power supply amount analysis module, which is used to perform electric vehicle demand analysis on the preset area, and obtain an electric vehicle demand power supply amount; a power supply optimization module, which is used to perform power supply optimization on the first energy station redundant electricity amount to the Nth energy station redundant electricity amount according to the electric vehicle demand power supply amount, obtain a building component energy station redundant power supply scheme, and execute building component energy station management.

[0009] The one or more technical solutions provided in the application have at least the following technical effects or advantages:

[0010] The method comprises the following steps: obtaining preset time zone weather information of a preset area; performing photovoltaic component energy storage analysis on a first building component energy station according to the preset time zone weather information to obtain first energy station predicted storage energy; performing photovoltaic component energy storage analysis on an Nth building component energy station according to the preset time zone weather information to obtain Nth energy station predicted storage energy; performing preset time zone power consumption analysis on the first building component energy station to the Nth building component energy station to obtain first building component energy station predicted power consumption to Nth building component energy station predicted power consumption; performing redundant power analysis on the first energy station predicted storage energy to the Nth energy station predicted storage energy according to the first building component energy station predicted power consumption to the Nth building component energy station predicted power consumption to obtain first energy station redundant power to Nth energy station redundant power; performing electric vehicle demand analysis on the preset area to obtain electric vehicle demand power supply; and performing power supply optimization on the first energy station redundant power to the Nth energy station redundant power according to the electric vehicle demand power supply to obtain a building component energy station redundant power supply scheme, and performing building component energy station management. That is, first, the preset time zone weather information of the preset area is obtained. Then, photovoltaic component energy storage analysis is performed on the first building component energy station to obtain predicted storage energy. This step is repeated until the analysis on the Nth building component energy station is completed, and the respective predicted storage energy is obtained. Then, the preset time zone power consumption of the first building component energy station to the Nth building component energy station is analyzed to predict the respective power consumption. Based on the predicted power consumption, redundant power analysis is performed to determine the redundant power of the first energy station to the Nth energy station. Next, the electric vehicle demand of the preset area is analyzed to obtain the power supply demand. Finally, according to the electric vehicle demand power supply, the redundant power of the first energy station to the Nth energy station is optimized to develop a redundant power supply scheme, and building component energy station management is performed. Through intelligent prediction analysis of photovoltaic energy station storage and power consumption, and combined with redundant power analysis for energy supply and demand balancing management, the redundant power of the energy station can be effectively utilized, the energy utilization rate is improved, and the goal of avoiding energy waste is achieved. Ultimately, through efficient management and optimized operation of the building component energy station, the technical effects of improving energy utilization efficiency and reducing operating costs are achieved, and green and sustainable energy supply is realized.

[0011] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the following specific embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions in the application or the prior art, the accompanying drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced. Obviously, the accompanying drawings in the following description are only exemplary and, for those skilled in the art, other drawings can be obtained without creative effort on the basis of the provided drawings.

[0013] Figure 1 A flowchart of a building component energy station management method fusing photovoltaic new energy according to the application;

[0014] Figure 2 A structural diagram of a building component energy station management system fusing photovoltaic new energy according to the application.

[0015] Explanation of reference signs:

[0016] The meteorological information acquisition module 11, the first energy storage prediction module 12, the Nth energy storage prediction module 13, the power consumption analysis module 14, the redundant power analysis module 15, the demand power supply analysis module 16, and the power supply optimization module 17. DETAILED DESCRIPTION

[0017] The application provides a building component energy station management method and system fusing photovoltaic new energy, which solves the technical problem in the prior art that, when redundant power is used to supply power to a trolleybus, the redundant power cannot be managed for energy supply and demand balance based on accurate energy storage prediction and power consumption demand, resulting in low refinement of energy management, further affecting efficient use of energy and stable power consumption of the trolleybus, and finally leading to energy waste. By managing the redundant power for energy supply and demand balance based on accurate energy storage prediction and power consumption demand, the refinement of energy management is effectively improved, the green and sustainable energy supply goal is achieved on the basis of ensuring stable power consumption of the trolleybus, and the technical effects of improving energy utilization efficiency and utilization rate and reducing energy waste are achieved.

[0018] The technical solutions in the application will be described clearly and completely below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the application, rather than all the embodiments of the application. It should be understood that the application is not limited by the exemplary embodiments described herein. Based on the embodiments of the application, all other embodiments obtained by those skilled in the art without creative effort belong to the scope of protection of the application. In addition, it should be noted that, for convenience of description, only parts related to the application are shown in the drawings rather than all parts.

[0019] Embodiment one, please refer to the accompanying Figure 1The application provides a building component energy station management method integrating photovoltaic new energy, wherein the method is applied to a building component energy station management system integrating photovoltaic new energy, and the building component energy station management method integrating photovoltaic new energy specifically comprises the following steps:

[0020] Step P10: obtaining preset time zone weather information of a preset area;

[0021] Specifically, by obtaining the weather information of a specific area, accurate prediction and management of the energy station are realized, which is an important basis for subsequent energy storage analysis, power consumption prediction and power supply optimization. Specifically, first, the selection of the target area is involved, that is, the geographical range to be analyzed is determined. Next, according to the geographical position and time zone of the area, relevant weather data are collected. These data may include temperature, humidity, wind speed, sunshine time, etc., which have a direct impact on the energy storage efficiency of photovoltaic components and the running state of electric vehicles. In addition, the collection of these weather information needs to use weather monitoring equipment or data services, which can provide real-time or predicted weather data to provide accurate data support for subsequent analysis. By obtaining these weather information, the energy storage of the photovoltaic energy station and the power consumption demand of the electric vehicle can be more accurately predicted.

[0022] Step P20: performing photovoltaic component energy storage analysis on the first building component energy station according to the preset time zone weather information to obtain a first energy station predicted energy storage amount;

[0023] Specifically, the weather information is used to analyze the photovoltaic component energy storage of the building component energy station to predict its energy storage amount, which provides a basis for subsequent efficient operation of the energy station and optimization of energy management. Specifically, first, the collected weather information needs to be combined with the characteristics of the photovoltaic components of the first energy station. Then, through analysis software or algorithm, the power generation efficiency of the photovoltaic components under different weather conditions is evaluated. In this way, the amount of electricity that the first energy station can store within a certain period of time can be predicted. In addition, this analysis process may also consider factors such as the installation angle of the energy station, the type and area of the photovoltaic panels, etc., to more accurately estimate the energy storage effect.

[0024] By obtaining the predicted energy storage amount of the first energy station, important reference for the management of the energy station is provided, which helps to develop reasonable energy dispatching strategies, ensures the stability and efficiency of energy supply, reduces energy waste, and realizes the double improvement of economic benefits and environmental benefits.

[0025] Step P30: performing photovoltaic component energy storage analysis on the Nth building component energy station according to the preset time zone weather information to obtain an Nth energy station predicted energy storage amount;

[0026] Specifically, each of the multiple building component energy stations is analyzed for photovoltaic module energy storage to predict the energy storage of each energy station. This continuous process ensures that all energy stations are thoroughly considered and analyzed, providing comprehensive data support for overall energy management. That is, starting from the first energy station, the same analysis process is performed for each energy station in sequence, including combining the characteristics of each energy station with the collected meteorological information, using analysis software or algorithms to evaluate the power generation efficiency of photovoltaic modules, and predicting the energy storage of each energy station within a certain time period. By obtaining the predicted energy storage of the Nth energy station, a comprehensive energy storage prediction for all energy stations can be provided, and it helps energy management personnel better understand the operation of each energy station, so as to develop more accurate and efficient energy management strategies.

[0027] Step P40: performing preset time zone power consumption analysis on the first building component energy station to the Nth building component energy station to obtain first building component energy station predicted power consumption to Nth building component energy station predicted power consumption;

[0028] Specifically, the demand of each energy station is predicted by analyzing the power consumption of multiple building component energy stations to ensure the balance between energy supply and demand, and to help improve energy utilization efficiency and optimize energy management. Specifically, the power consumption of each energy station is analyzed in detail. First, consider the historical power consumption data of each energy station, combine the meteorological information of the preset time zone, and evaluate the power consumption demand in different time periods. Then, use these data to predict the power consumption of the first energy station to the Nth energy station. In addition, factors such as the operating mode of the energy station and the load characteristics may also be considered to improve the accuracy of the prediction.

[0029] Step P50: performing redundancy power analysis on the first building component energy station predicted power consumption to the Nth building component energy station predicted power consumption based on the first building component energy station predicted power consumption to the Nth building component energy station predicted power consumption to obtain first energy station redundancy power to Nth energy station redundancy power;

[0030] Specifically, by analyzing the predicted power consumption and energy storage of each building component energy station, the redundancy power of the energy station is determined. By comparing the predicted power consumption of each energy station with the predicted energy storage, the redundancy power that each energy station may generate within a certain time period can be determined. By obtaining the redundancy power of the first energy station to the Nth energy station, important data basis is provided for the management of the energy station.

[0031] Step P60: performing electric vehicle demand analysis on the preset area to obtain electric vehicle demand power supply;

[0032] Specifically, the energy demand of the electric vehicle in the preset area is analyzed to predict the required power supply. First, the operation data of the electric vehicle is collected and analyzed, including the number of electric vehicles, the operation route, the operation time, the energy consumption rate, etc. Combined with the traffic flow of the preset area and the operation mode of the electric vehicle, the required power supply of the electric vehicle in a specific time period can be predicted. In addition, this analysis process may also consider factors such as the charging mode and battery capacity of the electric vehicle to improve the accuracy of the prediction. By obtaining the required power supply of the electric vehicle, important data basis is provided for the operation of the electric vehicle, thereby ensuring the stability and efficiency of the operation of the electric vehicle.

[0033] Step P70: According to the electric vehicle demand power supply, the first energy station redundant power supply until the Nth energy station redundant power supply is optimized to obtain the building component energy station redundant power supply scheme, and the building component energy station management is executed.

[0034] Specifically, the redundant power supply of multiple energy stations is optimized using the electric vehicle demand power supply to develop an efficient energy station power supply scheme. First, the electric vehicle demand power supply needs to be compared and analyzed with the predicted redundant power supply of each energy station. Then, through optimization algorithm or strategy, the power supply plan of the energy station is adjusted to maximize the use of redundant power supply to meet the demand of electric vehicle. Through intelligent prediction and analysis of photovoltaic energy station energy storage and power consumption, and combined with redundant power supply analysis for energy supply and demand balancing management, the redundant power supply of the energy station can be effectively utilized, and the goal of improving energy utilization rate and avoiding energy waste is achieved. Finally, through the efficient management and optimized operation of the building component energy station, the technical effects of improving energy utilization efficiency, reducing operation cost and realizing green and sustainable energy supply are achieved.

[0035] Further, step P20 of the present application comprises:

[0036] obtaining a photovoltaic component energy storage analysis model of the first building component energy station;

[0037] The preset time zone meteorological information includes daily average solar radiation intensity, daily average environmental temperature, daily average environmental humidity, daily average light duration, daily average cloud cover, and daylight time zone;

[0038] According to the photovoltaic component energy storage analysis model, the daily average solar radiation intensity, the daily average environmental temperature, the daily average environmental humidity, the daily average light duration, and the daily average cloud cover are processed, and the first energy station predicted daily energy storage amount is outputted;

[0039] Calculate the energy storage time zone step length in the preset time zone belonging to the daylight time zone;

[0040] The ratio of the energy storage time zone step and the sunlight time zone is calculated to obtain an energy storage day number, and the ratio of the energy storage day number and the predicted daily energy storage amount of the first energy station is calculated to be the predicted energy storage amount of the first energy station.

[0041] Specifically, by analyzing the energy storage of the photovoltaic module and processing meteorological information, the daily energy storage amount and the total energy storage amount of the first energy station are predicted.

[0042] First, a suitable model is developed or selected, which can predict the energy storage effect of the photovoltaic module according to the characteristics of the photovoltaic module, denoted as a photovoltaic module energy storage analysis model. Then, the preset time zone meteorological information is used as the key input data for the model to predict the energy storage amount, specifically including the daily average solar radiation intensity, the daily average ambient temperature, the daily average ambient humidity, the daily average sunlight duration, the daily average cloud cover, and the sunlight time zone. Among them, the preset time zone meteorological information is the meteorological factor that may affect the daily energy storage of the building component energy station, which is set by the relevant energy storage professional based on their own energy storage management experience and professional energy storage and management knowledge background. For example, under the same conditions, the greater the solar radiation intensity, the greater the energy storage amount of the building component energy station. Further, by analyzing these data that may affect the energy storage of the building component energy station through the photovoltaic module energy storage analysis model, the predicted daily energy storage amount of the first energy station is automatically output. The photovoltaic module energy storage analysis model can be an intelligent model obtained by supervised learning and training of historical building component energy storage data based on neural network principles. Next, the energy storage time zone step of the sunlight time zone in the preset time zone is calculated, and the ratio of the energy storage time zone step and the sunlight time zone is calculated to obtain the energy storage day number. Finally, according to the energy storage day number and the predicted daily energy storage amount of the first energy station, the predicted energy storage amount of the first energy station is calculated.

[0043] By predicting the energy storage capacity of the photovoltaic module under specific environmental conditions, and further combining the energy storage day number, the energy storage amount of the building component energy station can be specifically and intuitively quantified and predicted, which ultimately provides important reference information for the management of the energy station and helps to develop more scientific and reasonable energy scheduling strategies.

[0044] Further, the present application includes:

[0045] The model and position number of the photovoltaic module of the first building component energy station are obtained.

[0046] The first photovoltaic module energy storage analysis model construction data set is collected and the initial photovoltaic module energy storage analysis model is trained under the constraint of the model number of the photovoltaic module.

[0047] The second photovoltaic module energy storage analysis model construction data set is collected and the initial photovoltaic module energy storage analysis model is trained under the constraint of the position number of the photovoltaic module to obtain the photovoltaic module energy storage analysis model.

[0048] Specifically, before constructing the photovoltaic component energy storage analysis model for predicting the energy storage of a specific photovoltaic component, an initial photovoltaic component energy storage analysis model is first established with the component characteristics corresponding to the photovoltaic component as constraints, so as to improve the pertinence of the photovoltaic component energy storage analysis model trained subsequently in predicting the energy storage of the specific photovoltaic component, and improve the accuracy of the photovoltaic component energy storage analysis model and the effectiveness of the prediction results.

[0049] Specifically, by obtaining the model number and position number of the photovoltaic component of the first building component energy station and taking the model number and position number of the photovoltaic component as constraints, a more accurate energy storage analysis model is helped to be collected and trained. First, a first photovoltaic component energy storage analysis model construction data set is collected with the photovoltaic component model as constraints. Then, the initial photovoltaic component energy storage analysis model is trained using this data set. Next, a second photovoltaic component energy storage analysis model construction data set is collected with the photovoltaic component position number as constraints, and the initial photovoltaic component energy storage analysis model is further trained and optimized using this new data set to obtain the final photovoltaic component energy storage analysis model. By customizing the more accurate energy storage analysis model according to the specific model number and position number of the photovoltaic component, the influence of the model number or position number of the photovoltaic component on the energy storage of the photovoltaic component is avoided, and the accuracy of the photovoltaic component energy storage prediction is improved through targeted data collection and training.

[0050] Further, the present application comprises:

[0051] The first photovoltaic component energy storage analysis model construction data set is collected with the photovoltaic component model as constraints, wherein the first photovoltaic component energy storage analysis model construction data set includes a daily average solar radiation intensity record data set, a daily average ambient temperature record data set, a daily average ambient humidity record data set, a daily average light duration record data set, a daily average cloud cover record data set, and a daily energy storage record data set;

[0052] The first photovoltaic component energy storage analysis model is trained according to the first photovoltaic component energy storage analysis model construction data set, with the daily energy storage record data set as supervision and the daily average solar radiation intensity record data set, the daily average ambient temperature record data set, the daily average ambient humidity record data set, the daily average light duration record data set, and the daily average cloud cover record data set as inputs, to obtain a first photovoltaic component energy storage analysis primary model;

[0053] The daily energy storage record dataset is supervised, the daily average solar radiation intensity record dataset, the daily average ambient temperature record dataset, the daily average ambient humidity record dataset, the daily average light duration record dataset, and the daily average cloud cover record dataset are inputs, and a regression decision tree is trained according to the first photovoltaic module energy storage analysis model dataset to obtain a second photovoltaic module energy storage analysis primary model;

[0054] A fully connected regression neural network is trained according to the first photovoltaic module energy storage analysis model dataset to obtain a third photovoltaic module energy storage analysis primary model;

[0055] The outputs of the first photovoltaic module energy storage analysis primary model, the second photovoltaic module energy storage analysis primary model, and the third photovoltaic module energy storage analysis primary model are inputs of a BP neural network, and the initial photovoltaic module energy storage analysis model is trained under the supervision of the daily energy storage record dataset.

[0056] Specifically, in the process of iteratively optimizing the initial photovoltaic module energy storage analysis model by using the first photovoltaic module energy storage analysis model dataset and the second photovoltaic module energy storage analysis model dataset, first, a plurality of meteorological data records related to the photovoltaic module model are collected and used to obtain the first photovoltaic module energy storage analysis model dataset. This includes daily averages of solar radiation intensity, ambient temperature, humidity, light duration, and cloud cover, as well as daily energy storage records. Such a dataset provides comprehensive and detailed information for training the energy storage analysis model. Specifically, the daily energy storage record dataset is supervised, and the daily average solar radiation intensity, ambient temperature, humidity, light duration, and cloud cover record dataset is used as input, and a first photovoltaic module energy storage analysis primary model is obtained based on the support vector machine principle. Then, the same dataset and supervision method is used to train again based on the regression decision tree principle to obtain a second photovoltaic module energy storage analysis primary model. Next, the same dataset is used to train a fully connected regression neural network to establish a third photovoltaic module energy storage analysis primary model. In addition, the outputs of the three primary models are used as inputs of a BP neural network, and the initial photovoltaic module energy storage analysis model is trained again under the supervision of the daily energy storage record dataset. By combining the advantages of different machine learning models, a more comprehensive and accurate initial photovoltaic module energy storage analysis model is constructed, which can more accurately predict the energy storage of photovoltaic modules.

[0057] Further, the step P40 of the application comprises:

[0058] Obtaining seasonal feature information, weekly feature information, and time zone length feature information of a preset time zone;

[0059] Constrained by the seasonal feature information, the week feature information and the time zone length feature information, a plurality of samplings are performed to obtain a plurality of electric energy loss record data of the first building component energy station;

[0060] The centralized data mean statistics of the plurality of electric energy loss record data are performed to obtain the first building component energy station predicted power consumption;

[0061] The second building component energy station predicted power consumption to the Nth building component energy station predicted power consumption are obtained in the same way as the first building component energy station predicted power consumption.

[0062] Specifically, the first building component energy station predicted power consumption, the second building component energy station predicted power consumption to the Nth building component energy station predicted power consumption are obtained in sequence, which provides a data basis for subsequent calculation of the redundant power of each building component energy station.

[0063] By obtaining the seasonal feature information, the week feature information and the time zone length feature information of the preset time zone, important time-related background is provided for the prediction of electric energy loss, and then the data sampling is performed with these feature information as constraint conditions, which helps to improve the accuracy and applicability of the prediction. Specifically, first, a plurality of samplings are performed with the seasonal feature information, the week feature information and the time zone length feature information as constraints. Then, a plurality of electric energy loss record data of the first building component energy station are obtained through the samplings. Next, the centralized data mean statistics of the electric energy loss record data are performed to obtain the first building component energy station predicted power consumption. Further, a plurality of samplings are performed with the seasonal feature information, the week feature information and the time zone length feature information as constraints to obtain a plurality of electric energy loss record data of the second building component energy station. Next, the centralized data mean statistics of the electric energy loss record data are performed to obtain the second building component energy station predicted power consumption. In this way, the predicted power consumption of all building component energy stations including the Nth building component energy station is obtained.

[0064] By effectively predicting the electric energy loss of a plurality of energy stations according to different time features, the collected electric energy loss data is closer to the fact, and thus is more real and reliable, thereby providing a more accurate and effective information basis for subsequent redundant power calculation.

[0065] Further, the step P50 of the present application comprises:

[0066] A set of historical power supply loss ratio record values of the first building component energy station is obtained, wherein the historical power supply loss ratio record value represents the difference between the actual power supply and the loss of energy storage, and the ratio of the loss of energy storage;

[0067] The historical power supply loss ratio record value set is subjected to centralized data mean value statistics to obtain the power supply loss ratio of the first building component energy station;

[0068] According to the predicted power consumption of the first building component energy station, the loss energy storage prediction is performed based on the power supply loss ratio of the first building component energy station to obtain the loss energy storage prediction value of the first building component energy station;

[0069] The first energy station redundant power is obtained by subtracting the loss energy storage prediction value of the first building component energy station from the predicted energy storage amount of the first energy station.

[0070] The second energy station redundant power to the Nth energy station redundant power are obtained in the same way as the first energy station redundant power.

[0071] Specifically, the redundant power of the first building component energy station is predicted by analyzing historical data. First, the historical power supply loss ratio record value set of the energy station is obtained, which reflects the ratio of the difference between actual energy supply and loss energy storage to loss energy storage, and is a key indicator for evaluating the efficiency of the energy station.

[0072] First, the historical power supply loss ratio record value set is subjected to centralized data mean value statistics to obtain the power supply loss ratio of the first building component energy station. Then, based on the power supply loss ratio and the predicted power consumption of the first building component energy station, the loss energy storage prediction is performed to obtain the loss energy storage prediction value of the first building component energy station. Next, the redundant power of the first energy station is calculated by subtracting the loss energy storage prediction value from the predicted energy storage amount of the first energy station. Further, the redundant power of the second energy station to the Nth energy station is calculated in the same way.

[0073] The redundant power of each building component energy station is calculated by quantitatively analyzing and predicting the loss energy storage of each building component energy station, and combining the predicted energy storage amount of each building component energy station obtained by the foregoing analysis. Accurate prediction of redundant power helps to optimize energy distribution, reduce waste, and improve overall energy utilization efficiency.

[0074] Further, the step P60 of the present application comprises:

[0075] Obtain the seasonal feature information, the weekly feature information and the time zone length feature information of the preset time zone;

[0076] According to the seasonal feature information, the weekly feature information and the time zone length feature information, extract the charging record data of the charging pile in the preset area;

[0077] According to the charging record data of the charging pile, count the demand power supply amount of the electric vehicle.

[0078] Specifically, according to the time characteristic information, the demand power supply amount of the electric vehicle at a specific time is counted, and by obtaining the seasonal characteristic information, the week characteristic information and the time zone length characteristic information of the preset time zone, an important time background for analyzing the charging demand of the electric vehicle is provided. These characteristic information helps to more accurately understand and predict the charging behavior of the electric vehicle. Specifically, first, according to the seasonal characteristic information, the week characteristic information and the time zone length characteristic information, the charging pile charging record data of the preset area in a specific season, a specific week and a specific time zone length is extracted, which reflects the charging activities of the electric vehicle under different time conditions. Then, based on these charging pile charging record data, the demand power supply amount of the electric vehicle is counted. Such a statistical method can help to understand and predict the charging demand of the electric vehicle in different time periods, and provide a basis for the layout of the charging pile and energy management. By effectively analyzing the charging demand of the electric vehicle, it is of great significance to optimize the distribution and operation of the charging pile and improve the energy utilization efficiency. Accurate demand power supply amount statistics of the electric vehicle helps to better meet the charging demand of the electric vehicle and promote the popularization and use of electric vehicles.

[0079] Further, the application comprises:

[0080] According to the demand power supply amount of the electric vehicle, the first energy station redundant power to the Nth energy station redundant power are randomly scheduled to obtain a plurality of energy station scheduling schemes;

[0081] The normalized value of the loss of electric energy is taken as a first fitness index, wherein the first fitness index has a first weight;

[0082] The normalized value of the number of scheduled energy stations is taken as a second fitness index, wherein the first fitness index has a second weight;

[0083] The first fitness index and the second fitness index are added according to the first weight and the second weight to obtain a comprehensive fitness function;

[0084] According to the comprehensive fitness function, the minimum value of the plurality of energy station scheduling schemes is extracted to obtain the building component energy station redundant power supply scheme.

[0085] Specifically, the optimal redundant power supply scheme for multiple energy stations is generated by a random scheduling method. Among them, each redundant power supply scheme of each energy station schedules the redundant power of each energy station according to the demand power supply amount of the electric vehicle to generate a plurality of corresponding scheduling schemes. Further, in order to evaluate these schemes, two fitness indexes are introduced: the normalized value of the loss of electric energy and the normalized value of the number of scheduled energy stations, and the two fitness indexes have different weights respectively.

[0086] Specifically, first, according to the power supply amount of the electric vehicle demand, the redundant power of the energy station is randomly scheduled to generate a plurality of scheduling schemes. Then, taking the normalized value of the loss of electric energy as the first fitness index and giving the first weight, at the same time, taking the normalized value of the number of scheduled energy stations as the second fitness index and giving the second weight. Next, according to the two weights, the first fitness index and the second fitness index are weighted and calculated to construct a comprehensive fitness function. Finally, according to the comprehensive fitness function, the minimum value of the comprehensive fitness is extracted from the plurality of energy station scheduling schemes, and the scheduling scheme corresponding to the minimum value is matched in reverse, which is recorded as the optimal redundant power supply scheme of the corresponding building component energy station. By effectively evaluating and selecting the optimal energy station scheduling scheme, it is helpful to improve the energy utilization efficiency and reduce the electric energy loss, and it has important significance for optimizing energy management and improving power supply reliability.

[0087] In summary, the building component energy station management method provided by the present application has the following technical effects:

[0088] By obtaining the preset time zone weather information of the preset area; performing photovoltaic component energy storage analysis on the first building component energy station according to the preset time zone weather information to obtain first energy station predicted energy storage; until performing photovoltaic component energy storage analysis on the Nth building component energy station according to the preset time zone weather information to obtain the Nth energy station predicted energy storage; performing preset time zone power consumption analysis on the first building component energy station to the Nth building component energy station to obtain first building component energy station predicted power consumption to Nth building component energy station predicted power consumption; performing redundant power analysis on the first energy station predicted energy storage to the Nth energy station predicted energy storage according to the first building component energy station predicted power consumption to the Nth building component energy station predicted power consumption to obtain first energy station redundant power to Nth energy station redundant power; performing electric vehicle demand analysis on the preset area to obtain electric vehicle demand power supply; performing power supply optimization on the first energy station redundant power to the Nth energy station redundant power according to the electric vehicle demand power supply to obtain building component energy station redundant power supply scheme, and performing building component energy station management. That is, first, the preset time zone weather information of the preset area is obtained. Then, the first building component energy station is analyzed to obtain the predicted energy storage. Repeat this step until the Nth building component energy station is analyzed to obtain the respective predicted energy storage. Then, the preset time zone power consumption of the first building component energy station to the Nth building component energy station is analyzed to predict the respective power consumption. Based on these predicted power consumptions, redundant power analysis is performed to determine the redundant power of the first energy station to the Nth energy station. Next, the electric vehicle demand of the preset area is analyzed to obtain the power supply demand. Finally, according to the electric vehicle demand power supply, the redundant power of the first energy station to the Nth energy station is optimized to develop a redundant power supply scheme, and building component energy station management is performed. Through intelligent prediction analysis of photovoltaic energy station energy storage and power consumption, and combined with redundant power analysis for energy supply and demand balancing management, the redundant power of the energy station can be effectively utilized to achieve the goal of improving energy utilization and avoiding energy waste. Ultimately, through efficient management and optimized operation of the building component energy station, the technical effects of improving energy utilization efficiency and reducing operating costs are achieved, realizing green and sustainable energy supply.

[0089] In the second embodiment, based on the same inventive concept as the building component energy station management method incorporating photovoltaic new energy in the foregoing embodiments, the present application also provides a building component energy station management system incorporating photovoltaic new energy. Please refer to the accompanying Figure 2 , the building component energy station management system incorporating photovoltaic new energy comprises:

[0090] The weather information acquisition module 11 is configured to obtain the preset time zone weather information of the preset area.

[0091] The first energy storage prediction module 12 is used to perform photovoltaic module energy storage analysis on the first building component energy station according to the preset time zone meteorological information to obtain the predicted energy storage of the first energy station.

[0092] The Nth energy storage prediction module 13 is used to perform photovoltaic module energy storage analysis on the Nth building component energy station based on the preset time zone meteorological information to obtain the predicted energy storage of the Nth energy station.

[0093] The power consumption analysis module 14 is used to perform preset time zone power consumption analysis on the first building component energy station up to the Nth building component energy station to obtain the predicted power consumption of the first building component energy station up to the Nth building component energy station.

[0094] Redundant power analysis module 15 is used to perform redundant power analysis on the predicted energy storage of the first energy station up to the predicted energy storage of the Nth energy station based on the predicted power consumption of the first building component energy station up to the predicted power consumption of the Nth building component energy station, and to obtain the redundant power of the first energy station up to the Nth energy station.

[0095] The demand power supply analysis module 16 is used to perform trolley demand analysis on the preset area and obtain the trolley demand power supply.

[0096] The power supply optimization module 17 is used to optimize the power supply of the redundant power of the first energy station up to the redundant power of the Nth energy station according to the power supply demand of the trolley, obtain the redundant power supply scheme of the building component energy station, and perform building component energy station management.

[0097] Furthermore, the first energy storage prediction module 12 in the building component energy station management system integrating photovoltaic new energy is also used for:

[0098] Obtain the photovoltaic module energy storage analysis model of the first building component energy station;

[0099] The preset time zone meteorological information includes the daily average values ​​of solar radiation intensity, ambient temperature, ambient humidity, sunshine duration, cloud cover, and sunshine time zone.

[0100] Based on the photovoltaic module energy storage analysis model, the daily average values ​​of solar radiation intensity, ambient temperature, ambient humidity, sunshine duration, and cloud cover are processed to output the predicted daily energy storage capacity of the first energy station.

[0101] Calculate the energy storage time zone step size that belongs to the sunlight time zone in the preset time zone;

[0102] The ratio of the energy storage time zone step and the sunlight time zone is calculated to obtain energy storage days, and the ratio of the energy storage days and the first energy station predicted daily energy storage amount is calculated and set as the first energy station predicted energy storage amount.

[0103] Further, the first energy storage amount prediction module 12 in the building component energy station management system integrated with photovoltaic new energy is further used for:

[0104] The model number and site number of the photovoltaic component of the first building component energy station are obtained.

[0105] The first photovoltaic component energy storage analysis model construction dataset is collected with the model number of the photovoltaic component as a constraint, and an initial photovoltaic component energy storage analysis model is trained.

[0106] The second photovoltaic component energy storage analysis model construction dataset is collected with the site number of the photovoltaic component as a constraint, and the initial photovoltaic component energy storage analysis model is trained to obtain the photovoltaic component energy storage analysis model.

[0107] Further, the first energy storage amount prediction module 12 in the building component energy station management system integrated with photovoltaic new energy is further used for:

[0108] The first photovoltaic component energy storage analysis model construction dataset is collected with the model number of the photovoltaic component as a constraint, wherein the first photovoltaic component energy storage analysis model construction dataset includes a daily average solar radiation intensity record dataset, a daily average environmental temperature record dataset, a daily average environmental humidity record dataset, a daily average illumination time record dataset, a daily average cloud amount record dataset, and a daily energy storage amount record dataset.

[0109] The first photovoltaic component energy storage analysis primary model is obtained by training a support vector machine according to the first photovoltaic component energy storage analysis model construction dataset, with the daily energy storage amount record dataset as supervision and the daily average solar radiation intensity record dataset, the daily average environmental temperature record dataset, the daily average environmental humidity record dataset, the daily average illumination time record dataset, and the daily average cloud amount record dataset as input.

[0110] The second photovoltaic component energy storage analysis primary model is obtained by training a regression decision tree according to the first photovoltaic component energy storage analysis model construction dataset, with the daily energy storage amount record dataset as supervision and the daily average solar radiation intensity record dataset, the daily average environmental temperature record dataset, the daily average environmental humidity record dataset, the daily average illumination time record dataset, and the daily average cloud amount record dataset as input.

[0111] The third photovoltaic component energy storage analysis primary model is obtained by training a fully connected regression neural network according to the first photovoltaic component energy storage analysis model construction dataset.

[0112] Taking the outputs of the first photovoltaic module energy storage analysis primary model, the second photovoltaic module energy storage analysis primary model and the third photovoltaic module energy storage analysis primary model as inputs of a BP neural network, taking the daily energy storage record data set as supervision, the initial photovoltaic module energy storage analysis model is trained.

[0113] Further, the electricity consumption analysis module 14 in the building component energy station management system fusing photovoltaic new energy is further used for:

[0114] Obtaining seasonal feature information, week feature information and time zone length feature information of a preset time zone;

[0115] Taking the seasonal feature information, the week feature information and the time zone length feature information as constraints, performing sampling for several times to obtain several electric energy loss record data of the first building component energy station;

[0116] Performing centralized data mean statistics on the several electric energy loss record data to obtain the first building component energy station predicted electricity consumption;

[0117] Wherein, the second building component energy station predicted electricity consumption to the Nth building component energy station predicted electricity consumption are obtained in the same way as the first building component energy station predicted electricity consumption.

[0118] Further, the redundancy electricity analysis module 15 in the building component energy station management system fusing photovoltaic new energy is further used for:

[0119] Obtaining a historical power supply loss ratio record value set of the first building component energy station, wherein the historical power supply loss ratio record value represents the difference between actual power supply and loss energy storage, and the ratio of the loss energy storage;

[0120] Performing centralized data mean statistics on the historical power supply loss ratio record value set to obtain the first building component energy station power supply loss ratio;

[0121] According to the first building component energy station predicted electricity consumption, loss energy storage prediction is performed based on the first building component energy station power supply loss ratio to obtain a first building component energy station loss energy storage prediction value;

[0122] Using the first energy station predicted energy storage minus the first building component energy station loss energy storage prediction value to obtain the first energy station redundancy electricity;

[0123] Wherein, the second energy station redundancy electricity to the Nth energy station redundancy electricity are obtained in the same way as the first energy station redundancy electricity.

[0124] Further, the demand power supply amount analysis module 16 in the building component energy station management system integrated with photovoltaic new energy is further used for:

[0125] obtaining seasonal feature information, week feature information and time zone length feature information of a preset time zone;

[0126] extracting charging pile charging record data of the preset area according to the seasonal feature information, the week feature information and the time zone length feature information;

[0127] statistically obtaining the electric vehicle demand power supply amount according to the charging pile charging record data.

[0128] Further, the redundant power amount analysis module 15 in the building component energy station management system integrated with photovoltaic new energy is further used for:

[0129] randomly scheduling the first energy station redundant power amount to the Nth energy station redundant power amount according to the electric vehicle demand power supply amount to obtain a plurality of energy station scheduling schemes;

[0130] taking a normalized value of lost power as a first fitness index, wherein the first fitness index has a first weight;

[0131] taking a normalized value of the number of scheduled energy stations as a second fitness index, wherein the first fitness index has a second weight;

[0132] adding the first fitness index and the second fitness index according to the first weight and the second weight to obtain a comprehensive fitness function;

[0133] extracting a minimum value from the plurality of energy station scheduling schemes according to the comprehensive fitness function to obtain the building component energy station redundant power supply scheme.

[0134] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The foregoing Figure 1 The building component energy station management method and specific example in embodiment one are also applicable to the building component energy station management system integrated with photovoltaic new energy in the present embodiment. Through the foregoing detailed description of the building component energy station management method integrated with photovoltaic new energy, those skilled in the art can clearly understand the building component energy station management system integrated with photovoltaic new energy in the present embodiment. Therefore, in order to make the specification brief, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant part is described in the method part.

[0135] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Numerous modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other embodiments without the use of the inventive faculty. Therefore, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0136] It will be readily apparent to one skilled in the art that varying substitutions and modifications can be made to the application disclosed herein without departing from the scope and spirit of the application. Moreover, it is the intent that all such variations and modifications be considered as falling within the scope of the application, and that the application be limited only by the definitions contained in the appended claims.

Claims

1. A method for managing building component energy stations integrating photovoltaic new energy, characterized in that, include: Obtain meteorological information for the preset time zone of the preset area; Based on the meteorological information of the preset time zone, the photovoltaic module energy storage analysis is performed on the first building component energy station to obtain the predicted energy storage capacity of the first energy station. Until the photovoltaic module energy storage analysis of the Nth building component energy station is performed based on the meteorological information of the preset time zone, the predicted energy storage capacity of the Nth energy station is obtained. A preset time zone power consumption analysis is performed on the first building component energy station up to the Nth building component energy station to obtain the predicted power consumption of the first building component energy station up to the Nth building component energy station; Based on the predicted electricity consumption of the first building component energy station up to the predicted electricity consumption of the Nth building component energy station, a redundancy analysis is performed on the predicted energy storage of the first energy station up to the predicted energy storage of the Nth energy station to obtain the redundancy of the first energy station up to the Nth energy station. Perform trolley demand analysis on the preset area to obtain the trolley power demand; Based on the power demand of the electric vehicle, the redundant power of the first energy station up to the redundant power of the Nth energy station is optimized to obtain a redundant power supply scheme for the building component energy station, and the building component energy station management is executed. Specifically, based on the power demand of the trolley, the redundant power of the first energy station up to the redundant power of the Nth energy station is optimized to obtain a redundant power supply scheme for the building component energy station, and the building component energy station management is executed, including: Based on the power demand of the electric vehicle, the redundant power of the first energy station up to the redundant power of the Nth energy station is randomly scheduled to obtain several energy station scheduling schemes. The normalized value of power loss is used as the first fitness index, wherein the first fitness index has a first weight; The normalized value of the number of dispatched energy stations is used as the second fitness index, wherein the first fitness index has a second weight; Based on the first weight and the second weight, the first fitness index and the second fitness index are summed to obtain the comprehensive fitness function; Based on the comprehensive fitness function, the minimum value is extracted from the several energy station scheduling schemes to obtain the redundant power supply scheme for the building component energy station.

2. The method as described in claim 1, characterized in that, Based on the meteorological information of the preset time zone, the photovoltaic module energy storage of the first building component energy station is analyzed to obtain the predicted energy storage capacity of the first energy station, including: Obtain the photovoltaic module energy storage analysis model of the first building component energy station; The preset time zone meteorological information includes the daily average values ​​of solar radiation intensity, ambient temperature, ambient humidity, sunshine duration, cloud cover, and sunshine time zone. Based on the photovoltaic module energy storage analysis model, the daily average values ​​of solar radiation intensity, ambient temperature, ambient humidity, sunshine duration, and cloud cover are processed to output the predicted daily energy storage capacity of the first energy station. Calculate the energy storage time zone step size that belongs to the sunlight time zone in the preset time zone; Calculate the ratio of the energy storage time zone step to the sunshine time zone to obtain the number of energy storage days, and calculate the ratio of the number of energy storage days to the predicted daily energy storage of the first energy station, which is set as the predicted energy storage of the first energy station.

3. The method as described in claim 2, characterized in that, Obtain the photovoltaic module energy storage analysis model of the first building component energy station, including: Obtain the photovoltaic module model and photovoltaic module reference number of the first building component energy station; Using the photovoltaic module model as a constraint, a dataset is constructed by collecting the first photovoltaic module energy storage analysis model, and the initial photovoltaic module energy storage analysis model is trained. Using the photovoltaic module reference number as a constraint, a dataset is constructed by collecting data from the second photovoltaic module energy storage analysis model, and the initial photovoltaic module energy storage analysis model is trained to obtain the photovoltaic module energy storage analysis model.

4. The method as described in claim 3, characterized in that, The steps for constructing the initial photovoltaic module energy storage analysis model include: Using the photovoltaic module model as a constraint, a dataset for constructing the first photovoltaic module energy storage analysis model is collected. The dataset for constructing the first photovoltaic module energy storage analysis model includes a daily average solar radiation intensity record dataset, a daily average ambient temperature record dataset, a daily average ambient humidity record dataset, a daily average sunshine duration record dataset, a daily average cloud cover record dataset, and a daily energy storage record dataset. Using the daily energy storage record dataset as supervision, and the daily average solar radiation intensity record dataset, the daily average ambient temperature record dataset, the daily average ambient humidity record dataset, the daily average sunshine duration record dataset, and the daily average cloud cover record dataset as input, a support vector machine is trained based on the dataset constructed according to the first photovoltaic module energy storage analysis model to obtain the first photovoltaic module energy storage analysis level one model; Using the daily energy storage record dataset as supervision, and the daily average solar radiation intensity record dataset, the daily average ambient temperature record dataset, the daily average ambient humidity record dataset, the daily average sunshine duration record dataset, and the daily average cloud cover record dataset as input, a regression decision tree is trained based on the dataset constructed according to the first photovoltaic module energy storage analysis model to obtain the second photovoltaic module energy storage analysis first-level model; Based on the first photovoltaic module energy storage analysis model, a dataset is constructed to train a fully connected regression neural network to obtain the third photovoltaic module energy storage analysis first-level model. The initial photovoltaic module energy storage analysis model is trained using the outputs of the first photovoltaic module energy storage analysis primary model, the second photovoltaic module energy storage analysis primary model, and the third photovoltaic module energy storage analysis primary model as inputs to a BP neural network, and the daily energy storage record dataset as supervision.

5. The method as described in claim 1, characterized in that, Perform a preset time zone power consumption analysis on the first building component energy station up to the Nth building component energy station to obtain the predicted power consumption of the first building component energy station up to the Nth building component energy station, including: Obtain seasonal, weekday, and time zone length information for the preset time zone; Constrained by the seasonal feature information, the weekday feature information, and the time zone length feature information, several sampling operations are performed to obtain several power loss record data of the first building component energy station; By performing centralized data averaging on the aforementioned power loss records, the predicted power consumption of the first building component energy station is obtained. The method for obtaining the predicted electricity consumption of the second building component energy station up to the Nth building component energy station is the same as that for the predicted electricity consumption of the first building component energy station.

6. The method as described in claim 1, characterized in that, Based on the predicted electricity consumption of the first building component energy station up to the predicted electricity consumption of the Nth building component energy station, a redundancy analysis is performed on the predicted energy storage of the first energy station up to the predicted energy storage of the Nth energy station to obtain the redundancy of the first energy station up to the Nth energy station, including: Obtain the set of historical power supply loss ratio records for the first building component energy station, where the historical power supply loss ratio records represent the difference between actual energy supply and energy loss storage, and the ratio of the difference to the energy loss storage. The power supply loss ratio of the first building component energy station is obtained by performing centralized data mean statistics on the set of historical power supply loss ratio records. Based on the predicted electricity consumption of the first building component energy station, and based on the power supply loss ratio of the first building component energy station, the energy storage loss prediction is performed to obtain the predicted value of the energy storage loss of the first building component energy station. The redundancy of the first energy station is obtained by subtracting the predicted energy storage value of the first building component energy station from the predicted energy storage value of the first energy station. The method for obtaining the redundant power of the second energy station up to the Nth energy station is the same as that for the redundant power of the first energy station.

7. The method as described in claim 1, characterized in that, Perform trolley demand analysis on the preset area to obtain the trolley power demand, including: Obtain seasonal, weekday, and time zone length information for the preset time zone; Based on the seasonal characteristic information, the weekday characteristic information, and the time zone length characteristic information, extract the charging record data of the charging piles in the preset area; Based on the charging record data of the charging pile, the power demand of the electric vehicle is calculated.

8. A building component energy station management system integrating photovoltaic new energy, characterized in that, The steps for implementing the building component energy station management method integrating photovoltaic new energy as described in any one of claims 1 to 7 include: The meteorological information acquisition module is used to obtain meteorological information for a preset time zone in a preset area. The first energy storage prediction module is used to perform photovoltaic module energy storage analysis on the first building component energy station based on the meteorological information of the preset time zone, and obtain the predicted energy storage of the first energy station. The Nth energy storage prediction module is used to perform photovoltaic module energy storage analysis on the Nth building component energy station based on the preset time zone meteorological information to obtain the predicted energy storage of the Nth energy station. The power consumption analysis module is used to perform preset time zone power consumption analysis on the first building component energy station up to the Nth building component energy station to obtain the predicted power consumption of the first building component energy station up to the Nth building component energy station. The redundant power analysis module is used to perform redundant power analysis on the predicted energy storage of the first energy station up to the predicted energy storage of the Nth energy station based on the predicted power consumption of the first energy station up to the predicted power consumption of the Nth energy station, and to obtain the redundant power of the first energy station up to the Nth energy station. The demand power supply analysis module is used to perform trolley demand analysis on the preset area and obtain the trolley demand power supply. The power supply optimization module is used to optimize the power supply of the redundant power of the first energy station up to the redundant power of the Nth energy station according to the power supply demand of the trolley, obtain the redundant power supply scheme of the building component energy station, and perform building component energy station management.

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