CN117952569A - Public building collaborative energy supply management system based on multisource renewable energy sources - Google Patents
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Abstract
The invention relates to the field of collaborative energy supply, and discloses a public building collaborative energy supply management system based on multi-source renewable energy sources, which is used for solving the problem that when the energy distribution in a collaborative energy supply building changes, a selected data acquisition point is not representative any more.
Description
Technical Field
The invention relates to the field of collaborative energy supply, in particular to a public building collaborative energy supply management system based on multi-source renewable energy sources.
Background
Public building collaborative energy supply refers to a way of sharing energy resources among a plurality of public buildings through network connection or energy internet platforms to achieve efficient utilization and management of energy, and the way can comprise sharing renewable energy, sharing energy storage equipment and sharing an energy management system.
The energy sources of the public building are from various renewable energy sources including biomass, wind energy, solar energy and the like, the existing public building collaborative energy supply method is to set data acquisition points at important positions for collaborative energy supply by analyzing the energy consumption requirements of the collaborative energy supply building, but the method for setting the data acquisition points can cause that the positions of the data acquisition points which are set before are not representative any more due to the change of the energy consumption distribution of the collaborative energy supply building.
The present invention proposes a solution to the above-mentioned problems.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a public building collaborative energy supply management system based on multi-source renewable energy sources, which solves the problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A public building collaborative energy supply management system based on multi-source renewable energy sources comprises a collaborative energy supply building selection module, a data acquisition point setting module, a data monitoring and management module and an energy source allocation module, wherein the modules are connected through signals;
The collaborative energy supply building selection module is used for selecting a collaborative energy supply center through a building energy center mode method, carrying out weighted summation on average electric energy consumption, resource enrichment coefficients and energy supply distances, screening according to the size of a weighted summation result to obtain a collaborative energy supply building, and transmitting collaborative energy supply building data to the data acquisition point detection module.
The data acquisition point detection module is used for clustering the power consumption data of the collaborative energy supply building, calculating to obtain a power consumption coefficient, carrying out weighted summation on the power consumption coefficient, the humidity data and the danger coefficient, and obtaining the position of the data acquisition point according to the weighted summation result;
The data monitoring and managing module is used for collecting the data of energy consumption and supply in real time at a data collecting point, detecting abnormal conditions in real time, sending alarm information in time and transmitting the abnormal data to the energy allocation module;
the energy allocation module is used for analyzing the energy supply and demand conditions of the collaborative energy supply building according to the received abnormal data and carrying out energy allocation and optimization.
Preferably, the building energy center mode method is a method for evaluating building energy consumption and performance through computer simulation, and is used for predicting energy use conditions of a building under different design schemes, building equipment and energy systems.
Preferably, the resource enrichment coefficient is the renewable energy enrichment degree of the building to be screened, and the resource enrichment coefficient is obtained by collecting the illumination intensity and the wind intensity of the building to be screened and calculating according to the illumination intensity and the wind intensity.
Preferably, the energy supply distance is the distance between the building of the cooperative energy supply center and the building to be screened, longitude and latitude coordinates of the building to be screened and the building of the cooperative energy supply center are determined, the longitude and latitude coordinates are converted into radian representation, the radian representation of the building to be screened and the building of the cooperative energy supply center is brought into a HAVERSINE formula by using a HAVERSINE formula method, and the spherical distance between the building to be screened and the building of the cooperative energy supply center is calculated.
Preferably, the clustering step of the power consumption data includes:
using a distributed sensor network to install sensors in each area of a building, and monitoring power consumption data in real time;
dividing the building into n areas on average, collecting total power consumption data of the building and power consumption data of each area through a sensor, and taking the power consumption data of each area as data points;
classifying the regions according to the power consumption data of each region, determining the clustering number by using a contour coefficient method, clustering the power consumption data of each region by using a K-means clustering method, and obtaining a final cluster.
Preferably, the step of clustering the power consumption data of each region by using a K-means clustering method includes:
Determining a cluster number by using a contour coefficient method, and randomly selecting data points corresponding to the cluster number from the acquired power consumption data of each region to serve as an initial cluster center;
calculating the distance between each data point and each initial cluster center by using an Euclidean distance method, and distributing the data points to the initial cluster closest to the data points;
and calculating a new cluster center according to the obtained initial cluster, and repeating the clustering process until the cluster center does not change obviously any more, and obtaining a final cluster and a final cluster center.
Preferably, the power consumption coefficient is a ratio of a final cluster center to average power consumption data.
Preferably, the risk coefficient is a risk coefficient indicating that a data acquisition point is arranged in the area, the vertical height difference between the center of each area and the ground and the horizontal distance between the center of each area and the periphery of the building are acquired, and the risk coefficient is obtained through calculation through the vertical height difference and the horizontal distance.
The invention has the technical effects and advantages that:
The method comprises the steps of obtaining a collaborative energy supply index through calculation, screening buildings participating in collaborative energy supply according to the collaborative energy supply index, calculating an collectable index in real time, obtaining the position of a data collection point according to the collectable index, collecting the energy consumption and the supplied data at the data collection point in real time, detecting abnormal conditions in real time, sending alarm information in time, transmitting the abnormal data to an energy allocation module, analyzing the energy supply and demand conditions of the collaborative energy supply buildings, and allocating and optimizing energy.
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Fig. 1 is an overall flow chart of the present invention.
FIG. 2 is a flow chart of the collectable index calculation of the present invention.
Detailed Description
The embodiments of the present invention will be clearly and completely described below with reference to the drawings in the present invention, and the configurations of the structures described in the following embodiments are merely examples, and a public building collaborative energy management system based on multi-source renewable energy according to the present invention is not limited to the structures described in the following embodiments, and all other embodiments obtained by a person skilled in the art without making any creative effort are within the scope of the present invention.
The invention provides a public building collaborative energy supply management system based on multi-source renewable energy, which comprises a collaborative energy supply building selection module, a data acquisition point setting module, a data monitoring and management module and an energy allocation module, wherein the modules are connected through signals, and the data processing steps among the modules are as follows:
the collaborative energy supply building selection module is used for obtaining a collaborative energy supply index through calculation and screening buildings participating in collaborative energy supply according to the collaborative energy supply index;
The building screening can help identify buildings with lower energy utilization efficiency and higher energy consumption, the buildings are brought into the collaborative energy supply system, a system optimization strategy can be utilized, the energy is effectively utilized, the whole energy consumption is reduced, the buildings are screened to determine which buildings have larger energy collaborative energy supply potential, a tighter collaborative network is built, the collaborative efficiency is improved, and the sharing and the mutual assistance of energy resources are promoted.
The screening of the building can ensure reasonable scale and range of the collaborative energy supply system, avoid excessive expansion or unnecessary resource investment, help to reduce the construction, operation and maintenance costs of the system and improve the economic benefit of the system, and can avoid the building with larger energy supply and demand difference from being brought into the collaborative energy supply system by screening the building, thereby reducing system fluctuation and instability, which is helpful to ensure stable operation of the collaborative energy supply system and improve the reliability and safety of the system.
In this embodiment, it should be specifically described that the cooperative energy supply index calculating step includes:
A building energy center mode method is used, a building is selected as a center of cooperative energy supply according to the requirement of the cooperative energy supply, and the selected cooperative energy supply center is required to be provided with renewable energy equipment and energy storage facilities, wherein the building energy center mode is an energy management mode, and energy systems in the building are managed and controlled in a centralized manner so as to improve the energy utilization efficiency, reduce the energy consumption and reduce the influence on the environment;
The intelligent ammeter is used for detecting the electric energy consumption of the building in the last month, the average electric energy consumption of the building is calculated and obtained, the average electric energy consumption is recorded as an electric energy consumption coefficient, and the calculation formula is as follows Where EL is expressed as a power consumption coefficient and E is expressed as the power consumption of the building for one month;
Detecting and recording the illumination intensity and the wind intensity of the last month by using an illumination detector and a wind detector, and calculating to obtain a resource enrichment coefficient according to the illumination intensity and the wind intensity, wherein the calculation formula is as follows Wherein RS is expressed as a resource enrichment coefficient, IL is expressed as illumination intensity, and WP is expressed as wind intensity;
calculating the distance between the building and the collaborative energy supply center by using HAVERSINE formula, and recording as a distance coefficient;
The cooperative energy supply index is obtained through the calculation of the electric energy consumption coefficient, the resource enrichment coefficient and the distance coefficient of the building by weighted summation, and the calculation formula is as follows The SE is denoted as a co-energy index, the EL is denoted as an electric energy consumption coefficient, buildings with higher energy consumption are generally selected as candidates for participating in co-energy, because these buildings have larger energy saving potential, the energy consumption can be reduced more effectively through co-energy, the larger the electric energy consumption coefficient is, the higher the co-energy index is, the more abundant the renewable energy resources around the building are, the more suitable for participating in co-energy, the energy complementation and co-energy can be realized by sharing the renewable energy resources, DT is denoted as a distance coefficient, the buildings with closer distances and adjacent geographic positions are selected to participate in co-energy, the energy sharing and co-energy can be realized more easily by the buildings with close geographic positions, the energy transmission loss is reduced, a1, a2 and a3 are denoted as the electric energy consumption coefficient EL, the resource enrichment coefficient RS and the weight coefficient of the distance coefficient DT, and the numerical values of a1, a2 and a3 are not particularly calculated in this embodiment.
The step of calculating the distance between the building and the collaborative energy supply center by using HAVERSINE formula is as follows:
Determining longitude and latitude coordinates of a building and a cooperative energy supply center, for example, the longitude and latitude coordinates of a building A are (40.7128 DEG N,74.0060 DEG W), and the longitude and latitude coordinates of the cooperative energy supply center are (34.0522 DEG N,118.2437 DEG W);
Converting longitude and latitude coordinates into radian representation, wherein the calculation formula is as follows ，/>Where lat rad is expressed as the latitude of the building and lon rad is expressed as the longitude of the building, e.g., converting building A into radians，/>Converting the cooperative energy supply center C into radian/>，/>；
Calculating the spherical distance between the building and the collaborative energy center by using HAVERSINE formula, wherein the calculation formula is thatWherein DT is expressed as spherical distance between the building and the co-energy center, R is average radius of the earth, c is an auxiliary parameter in HAVERSINE formula, and its calculation formula is/>Wherein a is an auxiliary parameter, and the calculation formula is/>Where lat A and lon A are the latitude and longitude of the building, lat C and lon C are the latitude and longitude of the co-energy center, e.g., calculating the spherical distance between building A and co-energy center C, substituting the radian of building A and the radian of co-energy center C into the formula to obtain/>。
And comparing the cooperative energy supply index with a preset threshold, if the cooperative energy supply index is larger than the preset threshold, the building energy consumption is higher, and the renewable energy source is more sufficient, and judging that the building can participate in cooperative energy supply, and if the cooperative energy supply index is smaller than the preset threshold, judging that the building is unsuitable to participate in cooperative energy supply.
The data acquisition point detection module is used for calculating an acquirable index in real time and judging the position of the selected building data acquisition point according to the acquirable index;
Screening the appropriate data acquisition point locations can ensure accurate collection of building energy consumption, environmental conditions and other relevant data, which are the basis for collaborative energy system decision-making and optimization, so that accuracy and comprehensiveness are required to be ensured, and manpower, material resources and financial resources are required to be invested in setting the data acquisition points in the building. Through reasonable screening data acquisition points, unnecessary resource waste can be avoided, building management cost is saved, quality and reliability of data can be improved through carefully selected data acquisition points, high-quality data is the basis of analysis and decision, and efficiency and performance of a collaborative energy supply system can be improved.
By selecting proper data acquisition point positions, the data transmission and processing time can be reduced, the response speed of the collaborative energy supply system is improved, the method is very important for timely adjusting energy supply and managing building energy consumption, the positions and the number of the data acquisition points directly influence the intelligent decision-making capability of the collaborative energy supply system, and reasonable data acquisition point distribution can provide enough data support to help the system to achieve the aims of intelligent scheduling, optimizing energy utilization and responding to energy demands.
In this embodiment, as shown in fig. 2, the step of calculating the collectable index is as follows:
Using a distributed sensor network, wherein sensors are arranged in each area of a building and used for monitoring power consumption data in real time, the sensors can transmit the data to a central data processing center through a wired or wireless network, the distributed sensor network is a network system consisting of a plurality of sensor nodes distributed in space, the sensor nodes can be distributed in a wide building area and connected with each other through wireless communication and the like, and the monitoring, sensing and data acquisition tasks of environments, objects or events are completed together;
dividing the building into n areas on average, collecting total power consumption data of the building and power consumption data of each area through a sensor, and taking the power consumption data of each area as data points;
Classifying the regions according to the power consumption data of each region, determining the clustering number by using a contour coefficient method, clustering the power consumption data of each region by using a K-means clustering method, and obtaining a final cluster, wherein the contour coefficient method is a method for evaluating the clustering quality, and can help to determine whether the data points are correctly distributed to the cluster centers of the data points by combining the intra-cluster distance and the inter-cluster distance of the clusters;
collecting the power consumption data of the whole building, and calculating to obtain a power consumption coefficient according to the final cluster and the power consumption data of the whole building, wherein the calculation formula is as follows Where EC is expressed as a power consumption coefficient, E' is expressed as a final cluster center, E Are all is expressed as average power consumption data, and its calculation formula is/>Wherein E Total (S) is represented as the total power consumption data of the building;
detecting real-time humidity data of each area by using a humidity detector to serve as a humidity coefficient;
Collecting the vertical height difference between the center of each area and the ground and the horizontal distance between the center of each area and the periphery of the building, and calculating the danger coefficient through the vertical height difference and the horizontal distance, wherein the calculation formula is as follows Wherein DG is expressed as a risk coefficient, h is expressed as a vertical height difference between the center of the area and the ground, d is expressed as a horizontal distance between the center of the area and the periphery of the building, and the risk coefficient is higher when the vertical height difference is larger and the horizontal distance is smaller;
Calculating according to the power consumption coefficient, humidity coefficient and risk coefficient to obtain an collectable index, wherein the calculation formula is The GA is represented as an acquirable index, EC is represented as a power consumption coefficient, some areas may have higher power consumption, when selecting a data acquisition point, these important areas should be prioritized to ensure that the power consumption of a critical area is monitored and managed, the higher the power consumption coefficient is, the higher the acquirable index is represented as a humidity coefficient, the high humidity environment may affect the stability and reliability of the data acquisition device, which easily causes device failure or inaccurate data acquisition, so the higher the humidity coefficient is, the lower the acquirable index is, DG is represented as a risk coefficient, the higher the risk coefficient is, the greater the safety risk representing the acquisition point is, the higher the risk coefficient is regarded as an unsafe area, the higher the risk coefficient is, the acquirable index is, b1, b2, b3 is represented as a weight coefficient of the power consumption coefficient EC, the humidity coefficient DA and the risk coefficient, and the specific numerical values of b1, b2 and b3 are not specifically processed in this embodiment.
In this embodiment, it should be specifically described that the step of clustering the power consumption data of each region by using the K-means clustering method includes:
Determining the number of clusters by using a contour coefficient method, randomly selecting data points with the corresponding number of clusters from the collected power consumption data of each region as an initial cluster center, wherein the region data is [10, 20, 30, 42, 21, 66, 24, 12, 11], the initial cluster number is three, and the initial cluster center is [10, 20, 30];
calculating the distance between each data point and each initial clustering center by using Euclidean distance method, wherein the calculation formula is that And assigning the data points into the initial cluster closest to the cluster, e.g., data point [42], and calculating their distance/>, from the three initial cluster centers，/>，Then data point [42] is assigned into initial cluster 3;
Calculating new cluster center according to the initial cluster, repeating the above clustering process until no obvious change occurs in the cluster center, and obtaining final cluster and final cluster center, for example, the initial cluster 3 is [30, 42, 66], and calculating new cluster center as 。
Comparing the collectable index with a preset threshold, if the collectable index is smaller than the preset threshold, judging that the region is not suitable for setting the data acquisition point, if the collectable index is larger than the preset threshold, judging that the region has high energy consumption and safer position, setting the data acquisition point in the region, and marking the data acquisition point as the initial data acquisition point position.
Calculating the acquirable indexes of each area in real time, obtaining the area positions suitable for setting the acquisition points, screening out the area numbers identical to the initial data acquisition point positions, calculating the change rate of the acquisition points by the area numbers identical to the initial data acquisition point positions and the initial data acquisition point numbers, comparing the change rate of the acquisition points with a preset threshold value, if the change rate of the acquisition points is smaller than the preset threshold value, not carrying out the position change of the data acquisition points, and if the change rate of the acquisition points is larger than the preset threshold value, setting the data acquisition points again when the acquisition point positions are not representative.
The data monitoring and managing module is used for collecting the data of energy consumption and supply in real time at a data collecting point, detecting abnormal conditions in real time, sending alarm information in time and transmitting the abnormal data to the energy allocation module;
The data monitoring and management in the cooperative energy supply of the building plays an important role, the energy utilization condition of the building can be known in real time by monitoring the consumption condition of various energy sources and the generation condition of renewable energy sources in the building, data support is provided for energy source supply and demand matching and optimization, the data monitoring and management system can identify peak time and abnormal conditions of energy source consumption, timely give an alarm, help building management personnel take measures to reduce the energy source consumption, improve the energy source utilization efficiency, and based on the monitored energy source consumption and the generation condition, the analysis and optimization of energy source supply and demand matching can be carried out, the energy source supply and the energy source demand are reasonably arranged, and the building can meet the demands with the lowest energy source cost.
The data monitoring and management system can evaluate the energy-saving measures and the renewable energy source utilization effect, help building management personnel to know the energy-saving measures and the renewable energy source utilization condition, provide references for subsequent optimization and improvement, provide a large amount of energy source data and analysis results for the building management personnel, support decision making and management decisions, help the building management personnel to make reasonable energy source management strategies and measures, improve the energy source utilization efficiency of the building, and timely discover and solve problems by monitoring and managing the energy source consumption and the generation condition of the building in real time, thereby improving the energy source management level of the building, reducing the energy source consumption cost and promoting sustainable development.
In this embodiment, it needs to be specifically described that the step of detecting the abnormal situation in real time includes:
counting the screened area, and installing a sensor and data acquisition equipment at the central position of the area, wherein the sensor and the data acquisition equipment are used for acquiring the data of energy consumption and supply in real time;
Starting sensor equipment, starting to acquire various data in the building in real time, and transmitting the data acquired by the sensor to a data processing center through a wireless or wired network, so that the instantaneity and the accuracy are ensured;
Processing and analyzing the collected data, including data cleaning, denoising, anomaly detection, etc., to ensure accuracy and reliability of the data
According to the monitored energy consumption condition and renewable energy generation condition, analyzing and optimizing energy supply and demand matching, and ensuring that the building can realize efficient energy utilization;
Detecting abnormal conditions in real time, and sending alarm information in time once the abnormal conditions are found, such as excessive energy consumption and abnormal equipment operation, so that relevant personnel can take measures in time;
The processed data are displayed in a visual form, such as a chart, a curve and the like, and a report is generated at the same time, so that visual data analysis results and decision support are provided for building energy management staff;
The collected data is stored and managed, so that the safety and the integrity of the data are ensured, and meanwhile, the data are convenient to review and analyze in the future.
And the energy allocation module is used for analyzing the energy supply and demand conditions of the collaborative energy supply building according to the received abnormal data and carrying out energy allocation and optimization.
Energy supply and use can be reasonably arranged according to actual demands of buildings through real-time monitoring data for energy allocation and optimization, energy utilization efficiency is improved, energy waste is reduced, energy resources can be reasonably allocated through energy allocation optimization, energy cost is reduced, energy utilization benefits are improved, energy expenditure is saved for the buildings, equipment operation modes can be optimized, unnecessary energy consumption is reduced, energy conservation and emission reduction targets are achieved, influence on the environment is reduced, and comfort level, such as temperature, humidity and illumination, of the interior of the buildings can be ensured, the comfort level of the interior of the buildings is improved, and life and working environments of people are improved.
Energy allocation and optimization are carried out through real-time monitoring data, energy supply can be reasonably arranged, the building can be ensured to continuously and stably supply energy, the safety and reliability of energy supply are improved, the operation mode of equipment is optimized, the service life of the equipment is prolonged, the maintenance and repair cost of the equipment is reduced, the operation efficiency and reliability of the equipment are improved, the reasonable utilization of energy is realized, the dependence on traditional energy resources is reduced, the utilization of renewable energy is promoted, and the building industry is promoted to be converted to a sustainable development direction.
In this embodiment, it should be specifically described that the energy allocation and optimization steps are as follows:
Based on the real-time monitoring data, analyzing the energy supply and demand conditions of the building, including energy consumption, renewable energy generation and the like, and determining the matching relationship between the energy supply and demand;
According to the result of the energy supply and demand matching analysis, a reasonable energy allocation scheme is formulated, including the supply quantity and the use mode of the electric energy are adjusted so as to meet the actual requirements in the building;
according to the formulated allocation scheme, allocating and optimizing the energy sources in the building, including reasonably arranging energy source supply and use and optimizing the running mode of equipment so as to achieve the purposes of saving energy, reducing emission, reducing energy cost and the like;
After the allocation optimization is implemented, the energy source in the building is monitored in real time, problems are found and adjusted in time, and meanwhile, the effect of the allocation optimization is fed back in real time so as to ensure the implementation effects of the energy source allocation and the optimization scheme;
and continuously monitoring and analyzing the real-time data, and continuously improving and optimizing according to the real-time monitoring data and the allocation optimizing effect, so as to continuously improve the utilization efficiency and the management level of the building energy.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (8)
1. The public building collaborative energy supply management system based on the multi-source renewable energy source is characterized by comprising a collaborative energy supply building selection module, a data acquisition point setting module, a data monitoring and management module and an energy source allocation module, wherein the modules are connected through signals;
The collaborative energy supply building selection module is used for selecting a collaborative energy supply center through a building energy center mode method, carrying out weighted summation on average electric energy consumption, resource enrichment coefficients and energy supply distances, screening according to the size of a weighted summation result to obtain a collaborative energy supply building, and transmitting collaborative energy supply building data to the data acquisition point detection module;
The data acquisition point detection module is used for clustering the power consumption data of the collaborative energy supply building, calculating to obtain a power consumption coefficient, carrying out weighted summation on the power consumption coefficient, the humidity data and the danger coefficient, and obtaining the position of the data acquisition point according to the weighted summation result;
The data monitoring and managing module is used for collecting the data of energy consumption and supply in real time at a data collecting point, detecting abnormal conditions in real time, sending alarm information in time and transmitting the abnormal data to the energy allocation module;
the energy allocation module is used for analyzing the energy supply and demand conditions of the collaborative energy supply building according to the received abnormal data and carrying out energy allocation and optimization.
2. A multi-source renewable energy based public building collaborative energy management system according to claim 1 wherein: the building energy center mode method is a method for evaluating the energy consumption and performance of a building through computer simulation and is used for predicting the energy use condition of the building under different design schemes, building equipment and energy systems.
3. A multi-source renewable energy based public building collaborative energy management system according to claim 1 wherein: the resource enrichment coefficient is the renewable energy enrichment degree of the building to be screened, and is obtained by collecting the illumination intensity and the wind intensity of the building to be screened and calculating according to the illumination intensity and the wind intensity.
4. A multi-source renewable energy based public building collaborative energy management system according to claim 1 wherein: the energy supply distance is the distance between the building of the collaborative energy supply center and the building to be screened, longitude and latitude coordinates of the building to be screened and the building of the collaborative energy supply center are determined, the longitude and latitude coordinates are converted into radian representations, the HAVERSINE formula method is used, the radian representations of the building to be screened and the building of the collaborative energy supply center are substituted into the HAVERSINE formula, and the spherical distance between the building to be screened and the building of the collaborative energy supply center is calculated.
5. A multi-source renewable energy based public building collaborative energy management system according to claim 1 wherein: the clustering step of the power consumption data comprises the following steps:
using a distributed sensor network to install sensors in each area of a building, and monitoring power consumption data in real time;
dividing the building into n areas on average, collecting total power consumption data of the building and power consumption data of each area through a sensor, and taking the power consumption data of each area as data points;
classifying the regions according to the power consumption data of each region, determining the clustering number by using a contour coefficient method, clustering the power consumption data of each region by using a K-means clustering method, and obtaining a final cluster.
6. The multi-source renewable energy-based public building collaborative energy management system according to claim 5, wherein: the step of clustering the power consumption data of each region by using the K-means clustering method comprises the following steps:
Determining a cluster number by using a contour coefficient method, and randomly selecting data points corresponding to the cluster number from the acquired power consumption data of each region to serve as an initial cluster center;
calculating the distance between each data point and each initial cluster center by using an Euclidean distance method, and distributing the data points to the initial cluster closest to the data points;
and calculating a new cluster center according to the obtained initial cluster, and repeating the clustering process until the cluster center does not change obviously any more, and obtaining a final cluster and a final cluster center.
7. A multi-source renewable energy based public building collaborative energy management system according to claim 1 wherein: the power consumption coefficient is the ratio of the final clustering center to the average power consumption data.
8. A multi-source renewable energy based public building collaborative energy management system according to claim 1 wherein: the dangerous coefficient is a dangerous coefficient which indicates that a data acquisition point is arranged in the area, the vertical height difference between the center of each area and the ground and the horizontal distance between the center of each area and the periphery of the building are acquired, and the dangerous coefficient is obtained through calculation of the vertical height difference and the horizontal distance.
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