82 results about "Electricity demand forecasting" patented technology

The invention provides a power demand prediction method, which comprises the following steps of: 1, collecting a power load data set, and preprocessing the power load data set; 2, inputting a power load data set, and executing a crown porcupine optimization algorithm CPO to optimize dynamic parameters of a variational mode decomposition (VMD) algorithm; step 3, establishing a serial model xLSTM-Informer (x < LSTM >-Informer >); 4, performing data prediction by adopting an Informer model: inputting the features extracted by the extended long-short-term memory network xLSTM model into the Informer model to perform power demand prediction, and outputting a comparison result of a power demand prediction value and a true value; and 5, evaluating the power demand prediction model obtained in the step 4. According to the method, data with different characteristics can be flexibly processed, and relatively good prediction performance can be kept in different power demand scenes.

The invention discloses a power demand evaluation method, system and device based on deep learning, and a storage medium, and belongs to the technical field of power system intelligence and energy management, and the method comprises the steps: collecting data of multiple modes, and carrying out the preprocessing of the data; designing an adaptive weighting strategy according to the preprocessed data, weighting different modal data, constructing a deep learning model through weighted fusion data, and training the model; and outputting a prediction result of the power demand, and correcting the output result. According to the method, the dynamic weighting strategy of multi-modal data and a time window correction mechanism are fused, so that the adaptability of power demand prediction to complex environment changes is remarkably improved, and the load fluctuation characteristics of extreme weather can be accurately captured; and in combination with the time sequence modeling capability of the long-short-term memory network, the long-term dependency relationship and the multi-factor coupling rule are effectively learned, and the problem that a traditional model is insufficient in expression of periodic and sudden demand modes is solved.

The invention discloses a self-adaptive source network load storage integrated intelligent scheduling system and method, and the system comprises a data collection module, a prediction module, a multi-objective optimization module, an optimization algorithm module, a self-adaptive parameter adjustment module, a constraint processing module, and a result output module. The future power demand is predicted through the power demand prediction model, the future renewable energy power generation amount is predicted through the renewable energy power generation prediction model, and a multi-target model with cost, benefit and greenhouse gas emission and model constraint conditions are constructed; the multi-target model is optimized through a hybrid optimization algorithm model combining a genetic algorithm and a particle swarm optimization algorithm, algorithm parameters are dynamically adjusted, a global optimal solution is searched, the optimized power generation equipment output power, the optimized energy storage equipment charging and discharging power and the optimized power grid interaction power are output, and a scheduling decision is guided; according to the method, the optimization efficiency and robustness are improved, and the adaptive capacity and the multi-target optimization capacity of a power grid system are enhanced.

The invention is suitable for the technical field of power market intelligent management, and provides a multi-level power market user file dynamic collaborative management method and system, and the method comprises the steps: obtaining user information and historical power consumption data, extracting power consumption features, and constructing a multi-dimensional user portrait, thereby achieving the refined classification of household, commercial and industrial users; combining a cosine similarity algorithm to analyze a power utilization dependency relationship of different levels of users, and establishing a dynamic power utilization mode model library; and predicting the power demand based on the user behavior change and hierarchical relevance, and optimizing a hierarchical power supply strategy in real time. The system comprises an electricity utilization information acquisition module, an association analysis module, a demand prediction module and a strategy making module. According to the method, the problems of extensive user hierarchy division and insufficient dynamic coordination in the prior art are solved, the power demand prediction precision and the power supply strategy flexibility are improved, efficient configuration of resources and stable operation of a power grid are realized, and remarkable economic benefits and social benefits are achieved.

The invention discloses an energy storage equipment charging and discharging control system based on artificial intelligence. The system comprises a data acquisition module, a data preprocessing module, a power demand prediction module, an energy storage equipment charging and discharging prediction module, an equipment charging and discharging strategy optimization module and an energy storage equipment charging and discharging intelligent control module. The invention relates to the technical field of data processing of energy storage equipment, in particular to an energy storage equipment charge and discharge control system based on artificial intelligence, which innovatively proposes that the intelligent control of charge and discharge of the energy storage equipment is realized by predicting the power demand and the state of the energy storage equipment in real time and combining an optimization algorithm to adjust a charge and discharge strategy; the long-term behavior features and the short-term dynamic charging and discharging features are combined, and feature fusion is performed by using a dynamic weighting mechanism, so that the accuracy of energy storage equipment charging and discharging state prediction is improved; by improving the particle optimization algorithm for obtaining the optimal charging and discharging strategy of the energy storage equipment, the global optimal solution search capability is improved, and the optimal charging and discharging strategy of the energy storage equipment is obtained.

The invention discloses a power demand prediction method, system and equipment based on market behavior influence deduction and a conditional diffusion model in the technical field of power market operation and demand prediction. The method comprises the following steps: constructing a market subject profit function model according to obtained power demand influence factors, carding a causal relationship between power demands and the power demand influence factors, and performing system dynamics simulation verification on the power demands to obtain a power demand simulation evaluation result; screening the power demand influence factors according to an evaluation result, performing data preprocessing on a data set of the power demand key influence factors, and expanding the preprocessed data set by using a diffusion model to obtain an expanded data set; performing correlation analysis and frequency domain transformation on the expanded data set to obtain a correlation coding matrix and a frequency domain data set; and inputting the expanded data set, the correlation coding matrix and the frequency domain data set into a Transform model to obtain a power demand prediction result.

The invention relates to the technical field of data analysis, and discloses a power demand prediction method based on a complex network and a graph neural network, and the method comprises the steps: recognizing a coupling relation between multi-source time sequence feature data sets, and constructing a power demand complex network through the multi-source time sequence feature data sets and the coupling relation, the method comprises the following steps: performing feature learning on a power demand complex network through a graph neural network, generating a power demand prediction model, pruning the power demand prediction model to obtain a lightweight prediction model, and performing demand analysis on power data to be analyzed by using the lightweight prediction model to obtain a target prediction demand. And performing feature attribution analysis on the target prediction demand to obtain a feature contribution degree, generating a demand prediction reason according to the feature contribution degree, and generating a power demand prediction result according to the target prediction demand and the demand prediction reason. According to the method, the requirements of high precision, real-time performance and credibility on power demand prediction under the new-quality productivity development background are effectively met.

The invention discloses a monthly load prediction method fusing time-of-use electricity price elements and long and short term time sequence characteristics, and relates to the field of load prediction, and the method comprises the steps: building a monthly load prediction model through employing a knowledge migration algorithm based on a dual-channel time sequence neural network and a long and short term time sequence routing module; the dual-channel time sequence neural network comprises a multi-scale partitioning module, a Mamba module and a local window converter; key characteristic parameters of monthly loads are obtained; and inputting the key characteristic parameters into a monthly load prediction model, and predicting the monthly load. According to the invention, a scientific and reliable quantitative basis can be provided for power demand prediction and system planning decision.

The invention provides a deep learning-fused power demand multi-algorithm collaborative demand prediction method and system, and the system comprises a short-term power demand prediction platform, a medium and long term power demand prediction platform, a collection management platform, and a display analysis platform. According to the whale optimization algorithm, the generalization ability is improved by introducing chaotic mapping and a nonlinear convergence factor, the weight is optimized in combination with a multi-scale graph convolutional network, the number of nodes, the learning rate and the number of iterations of a model are optimized in combination with a simulated annealing algorithm, the model is constructed, and by setting a medium-and-long-term power demand prediction platform, the power demand prediction efficiency is improved. Based on the Tent chaotic mapping and the dynamic step length factor optimization standard sparrow search algorithm, the prediction result is accurate and reliable by optimizing the key parameters of the composite model combining the convolutional neural network and the bidirectional long and short term memory network with the attention mechanism, and the power demand prediction cost is reduced.

Provided are an information processing apparatus, an information processing method, and a program that improve the prediction accuracy of electricity prices while achieving consistency among a plurality of markets. 【Solution means】An information processing apparatus 1 includes an acquisition unit 10 that acquires information regarding operating power sources by applying a power demand prediction to a power source operation model, and a prediction unit 20 that predicts the electricity price in a first system by applying the information regarding the operating power sources to a first individual model, and predicts the electricity price in a second system different from the first system by applying the information regarding the operating power sources to a second individual model.

The invention relates to an orderly power utilization management method based on user behavior analysis, and belongs to the technical field of power utilization behavior analysis. The method comprises the steps of data acquisition, data analysis and prediction, calculation of a personalized power utilization strategy, dynamic excitation of the strategy and establishment of a user feedback platform. Through the method, the accuracy of power demand prediction can be improved, power resources are reasonably scheduled, and the participation degree and satisfaction degree of users are improved. Through refined load prediction and orderly power utilization guidance, the power utilization peak can be effectively stabilized, the peak load can be reduced, the safe and stable operation capability of the power grid can be improved, and powerful support is provided for power dispatching and emergency management.

The invention relates to the field of power customer demand prediction based on data mining, in particular to a power customer demand prediction method and system based on data mining, and the method comprises the steps: collecting multi-source data of a target customer group; carrying out power consumption behavior clustering on the clients based on pattern mining, and generating user behavior tags; extracting a multi-dimensional feature vector of the customer based on the behavior tag; a prediction model is constructed according to the multi-dimensional feature vectors, and accurate prediction and optimal management of power customer demands are realized by combining a data mining technology and power demand prediction. Firstly, a multi-source data acquisition technology is adopted, rich input data is provided for a prediction model, and power consumption behaviors of various clients can be reflected more accurately. And secondly, through mode mining and power consumption behavior clustering, the clients are divided into different power consumption groups, personalized features of the clients are generated based on behavior tags, accurate input feature vectors are provided for a subsequent prediction model, and the prediction accuracy is improved.

The invention relates to the technical field of smart power grids, and discloses a load regulation and control method based on multi-modal power utilization data fusion simulation, which comprises the following steps: data acquisition: acquiring multi-modal data including power utilization data, weather data, user behavior data and electricity price data through a sensor and a smart electric meter; data preprocessing: preprocessing the obtained multi-modal data, removing abnormal data and performing normalization processing; and load prediction: based on the preprocessed data, using a load prediction model to predict the power demand. According to the invention, through the load prediction model based on multi-modal data fusion, multi-strategy cooperative operation of load reduction, demand response, renewable energy consumption scheduling and the like, and real-time adjustment of the local control unit, the real-time adjustment is realized; the power demand prediction precision is remarkably improved, the use efficiency of power resources is optimized, the stability and the adaptive capacity of power grid operation are improved, and the defects of a traditional power dispatching method in a variable load environment are overcome.

The invention provides a continuous power supply method and system for a mobile shelter, and the method comprises the steps: comprehensively detecting an energy available type and an environment change trend through an environment sensing module, and generating environment energy data through combining with meteorological parameters such as temperature and humidity; a machine learning algorithm is adopted to deeply analyze the data and real-time power demands, and an optimal energy combination mode is predicted. Based on the mode, the intelligent load distribution mechanism adjusts the working state of the electric equipment to obtain an optimized equipment operation state. The self-adaptive control technology is used for dynamically adjusting the output proportion of each energy source, a battery management system is combined for monitoring the charging and discharging states of a battery, optimized energy source output configuration is formed, an auxiliary power generation unit (such as a flexible solar panel, a micro wind driven generator and a kinetic energy recovery system) intelligently supplements the surplus power demand, and finally an efficient power supply scheme is formed. According to the scheme, continuous, efficient and stable power supply of the movable square cabin is achieved.

The invention discloses a power demand prediction system and method based on a deep fusion network, and the method comprises the steps: carrying out the standardization preprocessing of multi-source data, introducing a self-adaptive weighting mechanism based on relation complexity, and dynamically fusing a plurality of statistical correlation coefficients to precisely recognize significant influence factors, the technical problem that traditional fixed weight screening is difficult to consider linear and nonlinear relationships is solved. Then, an Almong polynomial distribution lag model and coupling coordination degree analysis are utilized to quantify time lag contribution of influence factors and process feature interaction, and a final feature set including a dynamic conduction mechanism is constructed. And finally, through a deep fusion model integrating the convolutional neural network and the bidirectional long-short-term memory network, multi-scale local features and bidirectional long time sequence dependence are extracted in parallel, so that high-precision prediction of the power demand is realized, and the defects that an existing model is weak in generalization ability and difficult to capture a complex time sequence rule are overcome.

The invention discloses a power demand prediction method based on time sequence and external environment factors, relates to the technical field of data processing and prediction, and solves the problem that power potential cannot be systematically evaluated due to the fact that no system performs data analysis and prediction on a power market in the prior art. Customer basic information, business expansion information, power consumption behaviors and external environment factors are obtained, then a user business expansion influence power consumption demand multi-factor analysis model is constructed according to business expansion business subdivision, and a user business expansion influence power consumption demand multi-factor analysis model is established by combining the industry prosperity, the electric quantity change rate in the power behaviors, the capacity change amount in the customer basic information and other multi-dimensional information. According to the method, a power demand coefficient of a comprehensive evaluation property is obtained, then an ARIMA model is adopted for fitting prediction of the power demand coefficient of each business expansion business type in the future, prediction of the electric quantity potential of cities, industries and enterprises is realized, and a more accurate decision basis is provided for power industry management and enterprise operation.

According to the present invention, building information (104) includes: basic building-information (101) that includes information on the size of a building; maximum annual power that indicates power consumption at a time. within one year. when the power consumption of the building is at a maximum level; and a power consumption amount of the building during a predetermined period within the one year. A processing device (21) extracts, from past power data (103), power data of a similar building for which the basic building-information (101) is similar to that of a target building. The processing device (21) generates power data of the target building by correcting the power data of the similar building so that, for the power data of the similar building and the power data of the target building, there is a match between the maximum annual power consumption amounts and also between the power consumption amounts during the predetermined period.

The invention relates to the technical field of big data processing, in particular to a power demand prediction method, and the method comprises the steps: obtaining initial power data; performing data processing operation on the initial power data, and determining target power data corresponding to the initial power data; extracting power features in the target power data; inputting the power characteristics into a target power model, and in the power demand prediction method based on the big data, obtaining initial power data, and performing data processing on the initial power data to obtain target power data corresponding to the initial power data; by processing the initial power data, the accuracy of the power data is improved, and an accurate data basis is provided for subsequently obtaining prediction information corresponding to power features; furthermore, the electric power features in the target electric power data are extracted, and the electric power features are input into the target electric power model to obtain prediction information corresponding to the electric power features, so that the accuracy of electric power demand prediction is greatly improved.

The present application discloses a method, system and platform for charging pile operation management based on a cloud platform, which relates to the field of charging pile management. The method includes: generating a user profile for each charging station; predicting the power consumption of the charging station at a future time node according to the power redundancy of the target area and the user profile of the charging station, and determining whether the charging station is connected to a grid energy storage device; for one of the charging stations, if the charging station is connected to a grid energy storage device, determining a distribution strategy between the target charging pile in the charging station and the grid energy storage device according to the device information; generating a predicted result of the power demand of the target charging pile at a future time node according to the user profile of each charging station, the power redundancy of the target area and the distribution strategy; and obtaining a regulation strategy for the target charging pile in the charging station at a future time node according to the predicted result of the power demand. The present application can effectively improve the user charging experience.

This invention discloses an energy-saving and safe prefabricated substation, relating to the field of power systems. It includes: a power demand forecasting module: predicting future power demand and adjusting transformer operating parameters based on this demand; a quantum communication configuration module: selecting a quantum key distribution protocol based on the transformer operating parameters, deploying quantum communication equipment, connecting smart sensors installed at various nodes, establishing an energy management panel, and collecting operational intelligent data through the smart sensors; an intelligent sensor network deployment module: receiving and preprocessing the operational intelligent data collected by the sensors through the energy management panel, deploying smart nodes within the substation to form a distributed sensing network; and a local data analysis and decision-making module: completing the operational intelligent data analysis and decision-making process locally through the distributed sensing network. This invention, through its power demand forecasting module, achieves accurate prediction of future power demand.

The invention discloses an iron and steel industry power demand prediction method and system based on multi-source factors, and relates to the technical field of data processing and big data analysis, and the method comprises the steps: collecting the historical environment, port throughput, logistics, power demand and iron and steel industry chain associated data of a target region; obtaining multi-source factor standard subdata through space-time alignment, preprocessing and monthly granularity division; through correlation analysis and trend chart construction, screening a first electricity consumption correlation factor, and determining an electricity consumption correlation coupling factor pair and short-term accidental and long-term stable correlation dynamic factors; a prediction model is constructed based on fitting of a time function and a periodic function, data are collected in real time, correction is carried out in combination with sudden factors such as extreme weather and policy adjustment, and a steel industry power demand prediction value is output. The method has the advantages that deep fusion of multi-source factors is achieved, prediction is accurate and reliable, and effective support can be provided for power grid load management and production planning of the iron and steel industry.

The invention belongs to the technical field of electric power, and discloses a method for establishing a diversified power consumption prediction model for a micro-grid unit. The method comprises a step of constructing a power distribution system source network load storage layering and partitioning structure, a step of constructing a diversified power utilization demand model facing a micro-grid unit, a step of predicting distributed photovoltaic output based on space-time fusion, a step of predicting an electric vehicle charging load, and a step of predicting a demand side response load. The method has the main beneficial technical effects that the prediction precision under the new energy uncertainty condition is improved, and the intra-day short-term prediction accuracy reaches 90% or above; an aggregation calculation method of the charging load of the electric vehicle is established; a stripping method and a prediction model based on a daily reference load curve are provided, the correlation between the stripping method and the temperature is analyzed, and the accuracy of intraday short-term load prediction reaches 95% or above.

The application discloses a kind of electric power demand prediction method, device, medium and equipment.The application is by comprehensive historical electric power data and to be predicted day information, using mutual information method and cosine similarity principle to carry out depth optimization to historical data, constructs accurate electric power demand prediction index system.Further, the relative error method of first structure model is filtered out with the initial sample set related to the to-be-predicted day, and then the numerical similarity and shape similarity analysis of the second structure model are analyzed, and the electric power data that meet the short-term electric power demand prediction are refined.Finally, in combination with the to-be-predicted day and the index system, the first index system is selected, and these selected data are input into the well-trained long short-term memory model, and the powerful time series analysis capability of the model accurately outputs the electric power demand result of each period of the to-be-predicted day.The application improves the accuracy of short-term electric power demand prediction to solve the problem that the short-term electric power demand cannot be accurately predicted in the prior art.

The application discloses a power demand prediction method and a terminal during the Spring Festival, which comprises the following steps: inputting prediction data of a Spring Festival influence period of a prediction year into a power demand prediction model which does not consider the influence of the Spring Festival and only considers the influence of air temperature, outputting power consumption of the Spring Festival influence period of the prediction year without considering the influence of the Spring Festival, and then calculating predicted power consumption of the Spring Festival influence period of the prediction year based on power consumption of the Spring Festival influence period of historical years considering the influence of the Spring Festival and the power consumption of the Spring Festival influence period of the prediction year without considering the influence of the Spring Festival, so as to step by step predict the power consumption considering only the influence of air temperature and the power consumption influenced by the Spring Festival, and combine power consumption of the Spring Festival influence period of historical years, so as to more accurately predict power demand during the Spring Festival, and help power supply enterprises to better plan and dispatch power during the influence period of the Spring Festival.

The application discloses a power demand prediction method and system based on big data analysis, relates to the technical field of power systems, and mainly aims to solve the problems of poor prediction accuracy and effectiveness of existing power demand, and save power transmission effectiveness. The method comprises the following steps: determining load information to be demanded and time period average use data corresponding to the load information, wherein the time period average use data comprises average values of power scheduling use in multiple time periods; based on the load information, a corresponding demand prediction model is called, and the time period average use data is processed based on the demand prediction model to determine power demand reference data of a target time period, wherein the demand prediction model is obtained by training a deep learning network based on multi-time period use sample data; a reference time period of the target time period is determined, and the power demand reference data is adjusted based on the power demand extreme value and the power demand average value of the reference time period to obtain a power demand prediction result.

The invention discloses a big data measurement asset use portrait and demand prediction allocation method, and belongs to the technical field of electric power big data and asset optimization management, and the method comprises the steps: S10, constructing a database related to a target region, the database comprising a high-demand power data group and a low-demand power data group, acquiring power consumption data of the user from the high-demand power consumption data group; and S20, distinguishing the high-demand power consumption data group into a regular power consumption user group and an irregular power consumption user group, and calculating the proportion of the regular power consumption user group and the irregular power consumption user group in the high-demand power consumption data group. According to the method, the accuracy and the response speed of power demand prediction are improved and the dynamic change of a complex power system is effectively dealt with by constructing the database, distinguishing the power consumption behavior modes of the users, dynamically monitoring the power demand change, extracting key power consumption characteristics, quantifying the load fluctuation ratio and implementing hierarchical load management.

The invention relates to a technology in the field of neural network application, in particular to a demand prediction method based on multimode power adaptive space-time dependent learning, which comprises the following steps of: constructing a power decision network in an offline stage and randomly initializing a modeled demand prediction model; the demand prediction model is composed of a feature representation module, a time network module, a space network module, a space-time dependence network module, a space-time fusion network module, a demand prediction network module and a loss calculation module. Performing feature representation on the multimode power flow data by using a feature representation module, forming a core of a demand model through a time network module, a space network module, a space-time dependence network module and a space-time fusion module, and forming a demand prediction model; and in the online use stage, through a demand prediction model obtained through training, power flow demand prediction is realized based on the input multimode power state data.

The application relates to the technical field of central air conditioning system management, and discloses a dynamic power management system based on central air conditioning load characteristics, which mainly comprises a load characteristic analysis module, a power demand prediction module, an energy distribution optimization module and a control execution module. The load characteristic analysis module classifies load characteristics by analyzing historical operation data of the central air conditioning system; the power demand prediction module predicts future power demand in combination with load characteristic classification results and weather forecast data; the energy distribution optimization module generates an optimal energy distribution scheme according to predicted power demand and current power supply conditions by using a genetic algorithm; and the control execution module adjusts the operation of the central air conditioning system according to the optimization results. The system accurately analyzes load characteristics, predicts power demand and optimizes energy distribution, effectively reduces power cost, meets the power demand of the central air conditioning system, improves energy utilization efficiency, and has remarkable economic and environmental protection benefits.

This invention discloses a power dispatching method, system, equipment, and medium based on daily planning and simulated operation, belonging to the field of power dispatching technology. The specific steps are as follows: First, acquire real-time and historical power grid data to establish a power demand dataset. Second, establish a power demand forecasting model, perform time series analysis on historical and real-time power grid data, and forecast future power demand. Third, establish a multi-objective optimization function, and use an improved genetic algorithm to solve the multi-objective optimization function to obtain the optimal power dispatching scheme. Fourth, input the optimal power dispatching scheme into the simulation model for verification, and then issue the verified optimal power dispatching scheme for execution. This invention, through the closed-loop fusion of data-driven prediction, multi-objective intelligent optimization, and high-fidelity simulated operation, transforms the traditional static dispatching mode relying on human experience into a dynamic optimization and proactive defense intelligent decision-making mode. This improves the automated generation capability of dispatching plans and the level of safety pre-control.

The invention relates to a digital data processing technology of a power management system, in particular to a power demand analysis method and system under multi-source data fusion. The analysis method comprises the following steps: collecting and converging multi-source heterogeneous data; performing data fusion and standardization processing; carrying out multi-dimensional scene load characteristic modeling and trend analysis; performing layered and classified electricity demand prediction; and outputting a hierarchical classification prediction report. The analysis system comprises a multi-source data acquisition module, a data fusion processing module, a feature modeling and analysis module, a hierarchical prediction and optimization module and a visual display module. Compared with the prior art, the method has the advantages that a complete power utilization analysis data panorama is constructed, and the load feature recognition integrity is improved by more than 40%; the average absolute percentage error MAPE of short-term load prediction can be reduced to be within 3% and is improved by about 35% compared with a general modeling analysis method; and the efficiency of converting the prediction result into the action can be improved by 50%.