34 results about "Regional power" patented technology

The invention discloses a regional power load real-time allocation system and method based on artificial intelligence, and belongs to the technical field of power distribution. According to the method, after an electric load data set and an alarm condition data set are integrated, a multi-dimensional feature interaction matrix is decomposed based on a factorization machine, a high-order interaction relation between electric load features and alarm condition features is extracted, hidden variable modeling is introduced, accurate division of risk levels of different areas is further achieved, and the accuracy of the risk levels is improved. Compared with a traditional single-domain feature analysis method, the method has the advantages that complex nonlinear relations can be captured, potential influence rules of regional power anomalies on alarm risks can be mined, and experimental verification shows that the method is high in accuracy and high in reliability. According to the modeling method, the recognition accuracy of the high-correlation region is improved by more than 15%, and a more scientific basis is provided for resource allocation decisions.

The invention discloses a trans-provincial and trans-regional electric power medium and long term transaction continuous operation model analysis system, and belongs to the technical field of electric power. A data acquisition and preprocessing module acquires electric power transaction data, new energy power generation data, meteorological data and market rule change information in real time or regularly; the market dynamic monitoring and analysis module dynamically monitors and analyzes the market supply and demand relationship, the price trend and the transaction rule change based on real-time and historical data; the new energy adaptability evaluation module evaluates the influence of the fluctuation and uncertainty of the new energy power generation on the medium and long term transaction of the power by comparing the actual data and the prediction data of the new energy power generation; and the transaction strategy optimization module formulates an optimal transaction strategy and a quotation scheme for two transaction parties by constructing a multi-objective optimization model and synthesizing cost, risk and income. According to the invention, the accuracy of analysis and prediction of medium and long term transactions of trans-provincial and trans-regional electric power in which new energy participates is improved.

The invention provides a regional power grid equivalent damping coefficient evaluation method and system based on data collected by a synchronous phasor measurement device, and belongs to the technical field of power system parameter identification. The method comprises the following steps: firstly, selecting a frequency measurement result with relatively high data quality before and after a large disturbance event, and analyzing the influence of a determined component and a noise component of an active unbalance amount on a system frequency dynamic state in the large disturbance event; secondly, performing curve fitting on discrete frequency measurement results, and estimating a frequency response component caused by a noise component effect after a large disturbance event based on a superposition principle; on the basis, the equivalent damping coefficient of the regional power grid is estimated by combining the active power sudden change amplitude caused by the large disturbance event, the occurrence time of the large disturbance event and the inertia coefficient of the regional power grid.

The invention relates to the technical field of smart grids and discloses a method for regional power load forecasting, comprising collecting historical power consumption data; constructing a KANs model for power load forecasting, using a part of time characteristics, meteorological characteristics and power consumption data in the historical data as a training set and another part as a test set; inputting the training set into the KANs model to obtain an initial power load forecasting model; inputting the test set into the initial power load forecasting model for verification to obtain a trained power load forecasting model, inputting the time characteristics and meteorological characteristics of a to-be-forecasted area in a specific time period into the power load forecasting model to obtain a power load forecasting result; the method can be applied to the forecasting of power loads at various time granularities and has the characteristic of high accuracy.

The invention discloses a cross-regional power transmission and transformation project environmental protection and water conservation quota adaptation method. The method comprises the steps of 1, constructing a multi-dimensional regional feature factor library, and integrating landform data, soil type distribution data, annual average rainfall data, ecological protection zoning data and local environmental protection policy texts of provinces along a target project; when the scheme is implemented, environmental protection policies and water and soil conservation requirements of different regions are automatically adapted by establishing a regional characteristic factor library and a quota association rule, manual coordination errors are eliminated, and cross-regional engineering compliance is improved; geographic parameters such as topographic gradient, soil erosion degree and vegetation coverage are fused, the water conservation facility quota is dynamically adjusted in combination with real-time meteorological data, excessive design or insufficient protection is avoided, and the total engineering cost is reduced. The quota model is continuously optimized based on historical engineering data and a machine learning algorithm, an intelligent recommendation scheme is provided for a new project, the quota compilation period is shortened, and the environmental protection management efficiency is remarkably improved.

The invention discloses an intelligent calculation center comprehensive energy power distribution control system and control method, relates to the technical field of intelligent calculation center energy management, and solves the problems that power distribution is often carried out according to the priority of a power distribution area, and power distribution selection is lack of consideration of the applicable energy type of the area in the prior art. And the power distribution effect of the power distribution area is not good. Generating a regional power utilization priority based on regional data; generating a regional energy priority based on the regional data and the energy data; generating a power distribution scheme based on the regional power utilization priority and the regional energy priority; according to the invention, energy is supplied to each region based on the power distribution scheme, and the region priority and the region energy priority are considered, so that each region has an optimal power distribution scheme during power distribution, and the power distribution effect of the power distribution region is improved.

The application discloses a kind of carbon constraints under regional power system planning collaborative optimization method and system, method includes: the unit product cost of each department technical path of calculation terminal, the technical path occupancy rate is predicted using Logit selection model and inventory replacement model, and terminal energy consumption and carbon emissions are summarized and calculated;Based on terminal energy consumption, power load curve is constructed, and system operation simulation data is obtained by solving power production model;Multi-year power supply and demand side evolution path optimization model with minimum total cost in the whole cycle as the target is constructed, which is decomposed into long-term planning main problem and short-term operation sub-problem using Benders decomposition, under the condition of introducing total carbon, carbon intensity and department carbon emission constraint, the optimal evolution path meeting carbon constraint is output by iteratively solving through investment scheme transmission and feedback optimality cut or feasibility cut between main problem and sub-problem.The application realizes the collaborative optimization of supply and demand sides under carbon constraint.

The invention provides a carbon compensation method based on cross-regional power trade transfer, and relates to the technical field of energy environments. According to the method, through constructing a power transaction carbon emission reduction responsibility accounting model, an emission reduction effort-based carbon emission reduction responsibility allocation model and a carbon compensation cost benefit analysis model, accurate allocation and fair compensation of carbon emission reduction responsibility in cross-regional power transaction are realized. Through accurate responsibility accounting, responsibility allocation considering economic and technical contributions and scientific economical efficiency verification, the defects that in the prior art, responsibility allocation is one-sided, technical investment and economic benefit allocation are difficult to reflect, and economical efficiency verification is lacked are overcome; and a scientific, fair, economical and feasible solution is provided for carbon emission reduction responsibility distribution and compensation in cross-regional power transaction.

The present invention relates to a method for joint clearing of main and auxiliary resources of regional interconnected power grids considering the reserve capacity of power generation and consumption resources, which belongs to the technical field of regional power grid management, and includes the following steps: the power generation side and the power consumption side in the sub-region evaluate their own regulation capabilities; enter the day-ahead market declaration link, all power generators, user-side demand response and energy storage determine their own day-ahead bidding information, and determine the load information at the same time; the sub-regional power grid determines the preliminary day-ahead clearing plan and pushes the information to the regional center; the regional center conducts secondary coordination on each region, and returns the winning bid information to the sub-region after adjustment; each sub-region returns the winning bid information to the power generator, user-side demand response and energy storage operator, and each department preliminarily executes according to the day-ahead plan. The regional power grid market can be cleared more efficiently, and the clearing plans of various types of generator resources, user-side demand response resources and energy storage resources in the region in the day-ahead and real-time main and auxiliary markets can be optimized and calculated to promote the consumption of clean energy.

The invention discloses a climate change long-term trend assessment method based on periodic data decomposition, and relates to the technical field of climate change assessment, and the method comprises the steps: collecting multi-source climate data, and constructing a multi-source climate data time sequence; performing periodic decomposition, extracting disturbance of long-term trend, periodic fluctuation and short-term random fluctuation components on the long-term trend, optimizing the long-term trend, and generating a long-term change trend of the target climate; and for the influence of the new energy power volatility, the regional power generation efficiency and the power grid load matching degree, generating an influence evaluation result, and generating a visual report. According to the method, the technical problems that the climate trend prediction precision is insufficient and the multi-dimensional influence on the power system is difficult to accurately evaluate in the prior art are solved, and the technical effects of improving the climate trend prediction precision and accurately quantifying the comprehensive disturbance influence on the power system are achieved.

The invention discloses a regional power market subject income optimization decision-making method based on a collaborative intelligent model, and relates to the technical field of power market optimization, and the method comprises the steps: carrying out the preprocessing and feature extraction of original data, and generating a high-quality structured training data set; inputting the high-quality structured training data set into a long-short-term memory network model to carry out price interval prediction, and obtaining an electricity price prediction interval result of each time period in the future; inputting the high-quality structured training data set and a preset quotation level into a random forest model for bidding success probability modeling, and obtaining a bidding success probability value of each capacity section; when it is judged that the market is in a new market, a price interval prediction model and a bidding success probability model are initialized; and constructing a multi-target fitness function by using an electricity price prediction interval result and a bidding success probability value. According to the method, continuous model optimization is realized through feedback learning of market clearing results, so that collaborative optimization of profit maximization and risk control is realized in a dynamic game market environment.

The invention discloses a China power industry carbon quota allocation method giving consideration to inter-generational and inter-provincial fairness. The method comprises the steps of obtaining national carbon emission data over the years and power industry carbon emission data over the years; based on the intergenerational emission reduction model, according to the national carbon emission data over the years and the carbon emission data of the power industry over the years, predicting the carbon emission of the power industry in the tth year; and multiplying the carbon emission of the power industry in the tth year by the adjusted accumulated carbon intensity of the power industry in the set region to obtain the power industry carbon quota of the corresponding set region in the tth year. According to the method, the upper limit of the trans-generational carbon quota is set, a carbon transfer responsibility adjustment mechanism based on regional power dispatching is introduced, and the problem of carbon responsibility imbalance caused by power trans-regional dispatching is effectively relieved; meanwhile, the emission reduction economic cost of each time period and each region is quantified, and a data basis is provided for carbon regulation and control.

The invention provides a regional power grid voltage early warning method and device based on big data, relates to the field of smart power grids, and solves the problem that errors caused by social changes in historical data are difficult to accurately analyze when a power grid source load end state is predicted according to the historical data. And a time period with self-adaptive change is difficult to set for each region. The method comprises the following steps: planning a plurality of management and control time periods; when the historical data is more, determining the reference power consumption and the reference power generation amount of the next management and control time period based on the date type and the position of the management and control time period; according to the volatility of the historical data, the environmental data, the reference power consumption and the reference power generation, determining the standard power consumption and the standard power generation of the next management and control time period; when the historical data is less, standard electricity consumption and standard generating capacity are determined through the electricity analysis model; an early warning instruction is determined based on the standard electricity consumption and the standard power generation amount, and early warning is given; according to the invention, the accuracy of power grid source-load end data analysis can be improved.

The invention relates to a regional power development planning evaluation method based on multi-level fuzzy comprehensive evaluation, and the method comprises the steps: collecting the power development planning data of a target region; then constructing a plurality of hierarchical indexes according to the power development planning data, allocating weights to the hierarchical indexes, calculating numerical values of the indexes, dividing evaluation grades according to the numerical values, and setting fuzzy values; and constructing a fuzzy evaluation matrix of each level index according to the fuzzy values, finally calculating a fuzzy comprehensive evaluation vector of the plurality of level indexes according to the fuzzy evaluation matrix, and calculating a comprehensive evaluation score according to the fuzzy comprehensive evaluation vector to evaluate regional power development planning. According to the method, global evaluation optimization is carried out from the perspective of comprehensively considering multiple factors, and all aspects of the power system are comprehensively evaluated by constructing a multi-level fuzzy comprehensive evaluation model.

The invention relates to an intelligent arrangement response method and system based on a power network environment, and relates to the technical field of power networks, and the method comprises the steps: collecting cross-regional power transmission data in real time through edge nodes, training a preset federated prediction model according to the cross-regional power transmission data, and outputting a parameter update quantity set, transmitting the parameter update quantity set to a global aggregator of the cloud to generate a global optimization target; based on the global optimization target and the historical data, outputting and executing a cross-regional power transmission strategy; establishing a weighted Chebyshev sub-problem based on a cross-regional power transmission strategy, cross-regional power transmission loss minimization, power supply reliability and carbon emission constraint, and outputting a Pareto final solution set; and acquiring actual operation data in real time, comparing the actual operation data with the Pareto final solution set, outputting a deviation value, if the deviation value is greater than a threshold value, updating the local model corresponding to the deviation value greater than the threshold value, uploading the local model to a global aggregator, and regenerating a global optimization target.

The present invention relates to a decision-making method for reducing the carbon cost of regional power generation. Based on real-time monitoring and cloud-edge collaboration, carbon emission data is captured through a continuous flue gas emission monitoring system to ensure the timeliness and reliability of the detection data, providing a reliable data basis for cloud platform prediction and decision-making. An enterprise-level edge computing gateway is used to achieve local information collection and data transmission continuation in case of network disconnection, ensuring information integrity and data reliability. By combining the distribution of edge computing and the centralization of the cloud platform, accurate prediction of the enterprise's carbon emission cost for the current year is achieved. Through cloud-edge collaboration, numerous carbon emission enterprises are connected into a whole, realizing interconnection and interoperability among enterprises, achieving comprehensive carbon emission benefits on a large scale, and having a very significant emission reduction effect.

The present application relates to the technical field of power consumption prediction, and particularly relates to a regional power consumption intelligent prediction method based on multi-source heterogeneous data, which comprises the following steps: determining the power consumption prediction influence tendency for the next preset monitoring period based on the power change characteristic value; when it is determined that the next preset monitoring period is a strong influence tendency for power consumption prediction, determining that the power consumption is corrected by the elastic influence weight coefficient, and determining whether the prediction for the power consumption is qualified based on the energy change characteristic value; when it is determined that the prediction for the power consumption is abnormal, correcting the prediction parameter for the power consumption in the next preset monitoring period based on the regional industry change quantitative value, including identifying the main analysis data set, determining whether to increase the model training set or separate the prediction model based on the period quantitative value of the main analysis data, and performing targeted analysis and prediction on the power consumption according to the specific situation of the power elasticity coefficient of the region to be predicted, thereby improving the prediction efficiency of the power consumption in the region.

The present invention discloses a cross-region power peak-valley node complementary visualization method and device. The method includes: obtaining cross-region power characteristic data, generating a regional power characteristic plane visualization graph, including a load change region and a power supply adjustable region, taking the union thereof to determine a first filling region including: an adjustable region and a non-adjustable region, where the adjustable region and the non-adjustable region are respectively the power supply adjustable region and the load change region except for the intersection of the load change region and the power supply adjustable region; calculating the adjustable region and the non-adjustable region of any two different regions by using the peak-valley node complementary method, establishing a peak-valley node characteristic visualization graph, and performing complementary calculation for different days within a planned time to generate a spatio-temporal arrangement visualization graph of the power peak-valley complementary characteristics of different regions. By dividing the adjustable region and the non-adjustable region and using the peak-valley complementary method to generate a visualization graph, the present invention improves the phased allocation efficiency of cross-region power resources.

The application relates to the technical field of power grid data processing, and discloses a regional power consumption prediction method; the regional power consumption prediction method comprises the following steps: S1, first period data collection; S2, period characteristic classification determination; S3, characteristic classification prediction determination; S4, second period data collection; S5, period characteristic classification updating; S6, characteristic classification prediction updating; the application collects the data of the power consumption in a short period in the region, carries out characteristic classification according to the preliminary collection, completes prediction after the classification is finished, and thus can carry out preliminary power consumption prediction and subsequent power consumption operation and maintenance management; in the subsequent power consumption process, the power consumption data is collected again according to a certain period, characteristic updating and prediction updating are carried out, the operation and maintenance strategy can be continuously adjusted according to the real-time change of the power consumption in the region, the real-time performance of the operation and maintenance strategy is ensured, and the power supply of the region brings lower operation cost.

This invention relates to the field of power generation forecasting technology, specifically a regional power generation forecasting system that integrates power grid topology and geographic information. The system includes a topology association construction module that collects node power generation data to generate a power grid association structure; a regional grid division module that receives data and divides the power supply grid structure; a line path mapping module that compares geographic coordinates to generate a line grid path structure; a power generation trend estimation module that establishes a regional power generation change structure; and a power generation forecasting module that determines the distribution and outputs the forecast results. In this invention, a network topology association system is constructed by collecting node data. A refined spatial physical grid is divided based on power supply boundaries. Neighboring spatial sets are selected based on broken line features to establish spatial mapping links for transmission lines. The overall regional demand evolution trend is inferred based on node fluctuation patterns. A deep comparison of topological structural features and spatial geographic attributes is performed to reshape the power flow distribution pattern of the complex power grid system, thereby improving the accuracy of regional forecasting results.

The invention discloses an extreme geomagnetic induction current evaluation method and device based on a regional power grid, terminal equipment and a storage medium, and belongs to the field of power systems. The method comprises the following steps: acquiring geomagnetic disturbance data of each geomagnetic station of a regional power grid to be evaluated, geomagnetic conductivity data of a geomagnetic induction current region, power grid topological structure data and power grid equipment configuration parameters; constructing a ground conductivity model according to the ground conductivity data; according to the geologic structure corresponding to the regional power grid to be evaluated, the geomagnetic disturbance data and the earth conductivity model, an induction earth electric field under geomagnetic disturbance is obtained through calculation; constructing a regional power grid node simulation model according to the power grid topological structure data and the power grid equipment configuration parameters; and according to the regional power grid node simulation model and the induced geoelectric field, calculating to obtain geomagnetic induced current distribution of each node of the to-be-evaluated regional power grid. According to the invention, the problem of lack of geomagnetic induction current evaluation in the prior art can be solved.

The invention relates to the technical field of electricity consumption prediction, in particular to an intelligent regional electricity consumption prediction method based on multi-source heterogeneous data, which comprises the following steps: determining an electricity consumption prediction influence tendency for a next preset monitoring period based on an electric power change characterization value; when it is determined that the next preset monitoring period is the power consumption prediction strong influence tendency, it is determined that the power consumption is corrected through the elasticity influence weight coefficient, and whether prediction for the power consumption is qualified or not is determined based on the energy change characterization value; and when it is determined that prediction for the electricity consumption is abnormal, correcting prediction parameters for determining the electricity consumption in the next preset monitoring period based on a regional industry change quantized value, including identifying a main analysis data set, determining to add a model training set or separate a prediction model based on a periodic quantized value of the main analysis data, and determining to determine the electricity consumption in the next preset monitoring period. According to the specific condition of the power elastic coefficient of the to-be-predicted region, the power consumption is analyzed and predicted in a targeted manner, and the prediction efficiency of the power consumption in the region is improved.

The invention discloses a power grid disaster risk assessment method and system, and the method comprises the steps: determining a composite disaster and a disaster-inducing factor of a regional power grid, calculating the composite strength of the disaster-inducing factor of the composite disaster based on the disaster-inducing factor, and determining a regional digital elevation model based on a satellite image and a geographic information system corresponding to the regional power grid. Calculating a forward index and a reverse index of the regional power grid serving as a disaster-bearing body, and calculating a characteristic quantitative index of the regional power grid based on the forward index and the reverse index; the disaster risk coefficient of the regional power grid is calculated according to the disaster-inducing factor composite strength of the composite disasters, the terrain slope coefficient of the region and the characteristic quantitative index of the regional power grid, and in the disaster risk assessment process, multiple composite disasters, terrain characteristics and the characteristics of the power grid are comprehensively considered. The accuracy and reliability of disaster risk assessment are improved, so that more accurate and reliable disaster risk assessment is realized.

The invention discloses a cross-regional power transmission section capacity demand assessment method, electronic equipment and system, and relates to the technical field of power transmission planning, and the method comprises the steps: carrying out the sampling solving based on a high-fidelity steady-state mathematical model, and generating a scene set representing the steady-state operation of a plurality of future power systems; learning and mapping internally-associated high-dimensional structure manifolds in all steady-state operation scenes in the scene set through a physical information learning framework; analyzing geometric features of the high-dimensional structure manifold, and generating a power transmission planning index; according to a power transmission planning index, the capacity demand of a target cross-regional power transmission section is evaluated, and for a future power grid in which high-proportion renewable energy sources and flexible resources coexist, high-dimensional and nonlinear structural distortion caused by space-time dimension coordination interaction of multiple resources can be accurately captured and quantified. And evaluating the structural situation of the steady-state power flow of the power grid from a global view.

This invention discloses a method for refined simulation calculation of carbon emissions from regional power systems, belonging to the field of simulation calculation technology. The method includes the following steps: collecting regional power system data; initially calculating carbon emissions from the generation side, grid side, and user side; determining the carbon emission accounting boundary of the regional power system based on regional geographic information data; obtaining distributed storage data using distributed storage technology; extracting features from the topological data to obtain regional topological feature data; constructing a simulation calculation model by combining the distributed storage data with a neural network algorithm; outputting refined simulation data for carbon emissions from the regional power system; and correcting the refined simulation data by integrating environmental data and a random forest algorithm, finally outputting the corrected refined simulation data for carbon emissions from the regional power system. This invention achieves refined simulation calculation of carbon emissions from regional power systems throughout their entire lifecycle and across multiple dimensions.

The invention relates to an economic structure intelligent monitoring method and system based on power utilization big data, and the method comprises the following steps: S1, collecting and preprocessing multi-source data of a regional power system; s2, extracting multi-dimensional power features based on the standardized user power consumption behavior data; s3, constructing an industry classification and economic structure recognition model, obtaining the industry affiliation and confidence distribution of each user based on the multi-dimensional power feature vector set, and calculating the power consumption ratio of each industry according to the power consumption of different types of users in the region to form an industrial power consumption structure matrix; s4, according to the industrial power consumption structure matrix, establishing an industrial power consumption-output value regression model in combination with historical economic statistical data; s5, by solving the industry electric quantity-output value regression model, the relative output value proportion of each industry in the region is obtained; and S6, mapping a model solving result into a GIS (Geographic Information System). According to the invention, quasi-real-time intelligent monitoring of the economic structure is realized, and the sensing speed and the response capability of the economic operation situation are greatly accelerated.

The invention provides an extreme weather uncertainty-oriented regional energy power configuration optimization method and system, and the method comprises the steps: firstly determining a regional historical typical extreme scene based on historical extreme weather and synchronous power data; then, constructing a power gap optimization upper-layer model considering load uncertainty disturbance and new energy fluctuation, and obtaining a power gap value of the region in a planning period; finally, on the premise that the regional power supply safety requirement is met, the minimum economic cost and the minimum carbon emission serve as targets, and an energy power low-carbon optimization configuration lower-layer model is constructed; and a regional energy power configuration optimization scheme is obtained by solving the energy power low-carbon optimization configuration lower-layer model. By considering the power supply and demand conditions under the influence of the extreme weather, the safety guarantee and low-carbon optimal configuration of the regional energy power system under the extreme weather condition are realized, the capability of the power system for coping with regional extreme events is improved, and the structural optimization of a regional power supply and the improvement of the cleanliness of a power grid are promoted.

The invention relates to the technical field of data processing and analysis of an energy power system, in particular to a method and a system for calculating a full-life-cycle average carbon footprint factor of a regional power system. The invention discloses a method and a system for calculating a full-life-cycle average carbon footprint factor of a regional electric power system. The method comprises the following steps: constructing a calculation model of an electric power average carbon footprint factor CFP; carbon emissions of different power generation types in the whole life cycle are calculated, and the total amount of carbon emissions in the whole life cycle of power transmission and distribution is calculated; and finally, according to the central station acquisition data and the accounting models in the steps S1-S3, calculating the whole life cycle average carbon footprint factor CFP of the regional power system of the to-be-calculated region. Compared with the prior art, a product carbon footprint management system is constructed for supporting, and different subjects can conveniently check the carbon footprints generated by power production and consumption.

The invention provides an influence assessment method and device for emission factors of an electric power system, and can be applied to the technical field of electric power carbon emission. The influence evaluation method for the emission factors of the electric power system comprises the following steps: determining respective current evaluation data of a plurality of regions according to respective current power generation carbon emission and current power transmission quantity of the plurality of regions; constructing a current evaluation matrix based on the plurality of current evaluation data and the current power transmission quantity; based on a power transmission constraint condition between the current scene and a future scene, according to the current evaluation matrix and the current power transmission quantity, predicting to obtain a target power transmission quantity; determining respective target evaluation data of the plurality of areas according to the respective target power generation discharge amount and target power transmission amount of the plurality of areas; and according to the current evaluation data and the target evaluation data, an influence evaluation result of change information on the emission factors of the electric power system of the region is determined, and the change information represents the difference of the future scene relative to the current scene under the power transmission constraint condition.

The invention discloses a regional power load prediction method, system and device and a medium, and the method comprises the steps: obtaining the information of a region where a power system is located, and carrying out the dividing and clustering grouping of the power system based on the region information; obtaining an electrical load sequence of each clustering group, and carrying out relevance calculation in combination with environmental factors to obtain a relevance degree; constructing an electrical load prediction model based on the correlation degree, inputting the environmental factor prediction sequence into the electrical load prediction model to carry out grouped electrical load prediction, and obtaining grouped electrical load data corresponding to the to-be-predicted time; and accumulating the grouped electrical load data to obtain regional power system load prediction data corresponding to the to-be-predicted time. According to the method, the influence of various environmental factors on the electrical load is comprehensively considered, the data volume of prediction calculation is effectively reduced through clustering grouping, the prediction efficiency is improved, and the problems of insufficient consideration of multiple factors, complex data processing and low prediction precision in a traditional method are solved.