125 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 charging pile operation management method, system and platform based on a cloud platform, and relates to the field of charging pile management, and the method comprises the steps: generating a user portrait of each charging station; according to the electric energy redundancy of the target area and the user portraits of the charging stations, electric energy consumption of the charging stations at future time nodes is predicted, and whether the charging stations are connected to a power grid energy storage device or not is judged; for one charging station, if the charging station is connected to the power grid energy storage device, determining an allocation strategy between a target charging pile in the charging station and the power grid energy storage device according to the equipment information; generating a power demand prediction result of the target charging pile at the future time node according to the user portrait of each charging station, the electric energy redundancy of the target region and the distribution strategy; and obtaining a regulation and control strategy of the target charging pile in the charging station at the future time node according to the power demand prediction result. The charging experience feeling of a user can be effectively improved.

The invention discloses a power demand prediction and coal supply optimization system based on big data analysis, which remarkably improves the precision of power demand prediction by integrating multi-dimensional real-time data and applying a deep learning algorithm. Meanwhile, the coal supply chain is optimized in combination with mixed integer linear programming and a genetic algorithm, cost minimization is achieved, and dynamic adjustment is conducted through a particle swarm optimization algorithm under the emergency condition. And a feedback learning module of the system performs adaptive adjustment by using a reinforcement learning mechanism, so that the real-time response capability is improved. In addition, the intelligent decision making module provides data visualization and regulation and control suggestions so as to support accurate decision making. On the whole, the problems of inaccurate power demand prediction, insufficient supply chain scheduling response and weak intelligent decision support are effectively solved, and efficient resource scheduling and power supply and demand balance in a complex scene are realized.

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 discloses an electric power prediction method based on a demand situation awareness fusion model, and relates to the technical field of electric power demand prediction, and the method comprises the steps: S1, carrying out the sampling of power consumption behavior data and environment power consumption data in a region, and carrying out the segmentation of the data, and obtaining a training set and a test set; s2, carrying out abnormal value processing on the training set, and taking the processed training set as the input of a base learner to obtain a first training feature; s3, performing dynamic clustering on the first training features, selecting an optimal feature to perform feature fusion with a target variable to obtain a target feature, and training a meta-learner through the target feature to construct a demand situation awareness fusion model; s4, the demand situation awareness fusion model responds to the test set to obtain a correction factor, and the model is corrected according to the correction factor to obtain a target demand situation awareness fusion model; s5, the target demand situation awareness fusion model responds to the real-time feature data to obtain a regional power demand result; and the power consumption prediction precision and reliability are obviously improved.

The invention discloses an energy dual-carbon gpt-based power demand prediction method and system, and the method comprises the following steps: obtaining historical power data which comprises historical charging data and historical power demand data of an electric vehicle; extracting charging demand characteristics based on historical charging data of the electric vehicle; constructing an electric vehicle charging demand prediction model according to the charging demand characteristics, and outputting a charging demand prediction value by the model; calculating a power demand-charging demand correlation coefficient between the historical power demand data and the historical charging demand data of the electric vehicle; training a preset power demand prediction model, inputting the charging demand prediction value into the trained power demand prediction model, and outputting a power demand prediction result of the same timestamp by the model; and calculating an error between the power demand prediction value and a historical actual value, comparing the error with a preset standard range, and if the error is within the standard range, applying the prediction method to the power demand prediction system.

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 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 the load characteristic classification result and weather forecast data; the energy distribution optimization module adopts a genetic algorithm to generate an optimal energy distribution scheme according to the predicted power demand and the current power supply condition; and the control execution module adjusts the operation of the central air-conditioning system according to the optimization result. According to the system, by accurately analyzing the load characteristics, predicting the power demand and optimizing the energy distribution, the power cost is effectively reduced, the power demand of the central air-conditioning system is met, the energy utilization efficiency is improved, and remarkable economic and environment-friendly benefits are achieved.

The invention discloses an energy efficiency optimization management method and system for power terminal users, and relates to the technical field of energy management. The method comprises the following steps: acquiring a power utilization data set; synchronizing to a time sequence prediction channel to carry out power consumption prediction on a power terminal user, and determining a plurality of power demand prediction data; determining a plurality of power utilization behavior modes; performing power utilization analysis, and formulating a first power utilization management strategy; simulating execution of the first power consumption management strategy to perform multi-target feedback, generating a feedback response data set, dynamically adjusting the first power consumption management strategy, determining a power consumption optimization management strategy, and performing self-adaptive optimization management of power consumption energy efficiency on a power terminal user. The technical problem that in the prior art, power terminal user energy efficiency management lacks real-time adjustment capacity is solved, self-adaptive optimization management is achieved by constructing a power utilization collection network, a time sequence prediction channel and a dynamic adjustment strategy, and the technical effect of improving the power utilization efficiency of power terminal users is 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 a multi-dimensional power demand prediction method and device, a storage medium and electronic equipment, and relates to the technical field of power demand prediction. Multi-dimensional power demand prediction can be realized, and a reliable power demand prediction result is obtained. The method comprises the steps that electric power data of a target area are acquired, the electric power data comprise historical generating capacity and total distant view energy consumption, and the total distant view energy consumption represents a predicted value of an energy consumption demand of the target area within a preset duration; a data set of historical power generation is determined, power demand short-term prediction is carried out according to the data set, a short-term power generation prediction value of the target area is obtained, and the data set is obtained by arranging the historical power generation according to time; performing power demand medium-term prediction according to the data set and a preset sliding window to obtain a medium-term generating capacity prediction value of the target area; and performing long-term power demand prediction according to the total long-range energy consumption to obtain a long-term power generation prediction value of the target area.

The invention discloses an intelligent analysis control system for power transmission operation, and relates to the field of power transmission operation control. Historical power demand data and influence factor data are collected, corresponding timestamps are recorded respectively, and original data are obtained; preprocessing the original data to obtain first data, and dividing the first data into a training sample set and a verification sample set; a multi-layer perceptron model is constructed, and a training sample set is used for training the model; using the verification sample set to calculate a mean square error as an evaluation index to evaluate the accuracy of the model, and obtaining an optimal prediction model; inputting the obtained influence factor data corresponding to the prediction time point into the optimal prediction model, and calculating a power demand prediction value of the prediction time point; the maximum bearing capacity of the power transmission system is obtained, the load rate of the power transmission line is calculated according to the power demand prediction value at the prediction time point, and evaluation and early warning are conducted on the power transmission system.

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.

The invention discloses a power demand prediction method and device, a medium and equipment. According to the method, the historical power data and the to-be-predicted day information are integrated, the historical data are deeply optimized by using the mutual information method and the cosine similarity principle, and an accurate power demand prediction index system is constructed. Furthermore, an initial sample set related to a to-be-predicted day is screened out through a relative error method of the first structure model, and electric power data meeting short-term electric power demand prediction are extracted through numerical similarity and shape similarity analysis of the second structure model. And finally, selecting a first index system in combination with the to-be-predicted day and the index system, inputting the selected data into the training primitive long-short-term memory model, and accurately outputting the power demand result of each time period of the to-be-predicted day according to the powerful time sequence analysis capability of the model. According to the invention, the accuracy of short-term power demand prediction is improved, and the problem that the short-term power demand cannot be accurately predicted in the prior art is solved.

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 discloses a power demand prediction method and a related device. The method comprises the following steps: acquiring a plurality of influence factors influencing a power demand; determining an original data set corresponding to each influence factor in the plurality of influence factors in a preset area; performing correlation analysis on the plurality of original data sets through a grey correlation analysis method to screen the plurality of influence factors to obtain target key influence factors; determining data corresponding to the target key influence factor in the plurality of original data sets to obtain a sample data set; training a preset power demand model according to a preset expert experience set and the sample data set to obtain a target power demand prediction model; and inputting a target data set corresponding to the target key influence factor in the preset area into the target power demand prediction model to obtain a target power prediction result. According to the invention, the method achieves the accurate prediction of the power demand.

The invention provides a shelter equipment remote power supply monitoring method and system based on the Internet of Things. The method comprises the following steps: monitoring each electric device in real time, and sending monitoring data to a cloud server to obtain real-time monitoring data; generating an accurate power demand prediction report according to the real-time monitoring data; based on the power demand prediction report, when power supply and demand imbalance is predicted, starting an optimization scheduling strategy based on a particle swarm optimization algorithm, dynamically adjusting the output power of a generator set or the working schedule of electric equipment, and evaluating potential risk points by using a fault tree analysis technology; generating an emergency response plan and an optimal scheduling result; and carrying out automatic management on a power resource shortage condition based on the optimal scheduling result, ensuring normal operation of high-priority equipment, and generating a power management decision suggestion. According to the technical scheme provided by the invention, the flexibility of power supply monitoring can be improved.

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 discloses a power demand prediction method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a plurality of power demand data sets of a preset region in a plurality of historical time periods; constructing a linear regression prediction model based on the plurality of power demand data groups and the plurality of historical time periods; inputting the target time period into a linear regression prediction model to obtain a first power demand value corresponding to the target time period; obtaining new energy electric vehicle charging demand data and electric appliance electricity demand data in a first preset time period in a preset area; the ending time of the first preset time period is earlier than the starting time of the target time period; determining a target adjustment coefficient based on the new energy electric vehicle charging demand data and the electric appliance electricity demand data; and adjusting the first power demand value based on the target adjustment coefficient to obtain a target power demand value. By adopting the embodiment of the invention, the accuracy of the predicted value of the power demand is improved.

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.

The invention discloses a power demand prediction method and device based on a fuzzy autoregressive distributed lag model. The method comprises the steps that the historical total carbon emission amount of a target area in a target time period, historical carbon emission attribute data of all to-be-selected influence factors and a historical actual power demand value are acquired; decomposing the total historical carbon emission into a carbon emission decomposition relational expression among the historical carbon emission attribute data of the to-be-selected influence factors; on the basis of the carbon emission decomposition relational expression and historical carbon emission attribute data, determining the influence contribution amount of each to-be-selected influence factor; determining a power demand prediction influence factor based on the influence contribution amount; acquiring carbon emission power attribute data corresponding to the power demand prediction influence factors, and acquiring carbon-free power attribute data irrelevant to carbon emission; and inputting the carbon emission power attribute data and the carbon-free power attribute data into a fuzzy autoregressive distributed lag model for power demand prediction to obtain a power demand prediction result.

The invention discloses a distributed methanol power generation method and device, and relates to the technical field of fuel cell power generation, and the method comprises the steps: carrying out the power demand prediction through historical power data, power grid load information and real-time environmental factors, generating a methanol supply scheme, and dynamically controlling the methanol pumping of a target independent power generation unit; operating parameters of the reforming reactor are adjusted by using a bidirectional optimization feedback mechanism to obtain an optimal reforming scheme, and hydrogen-rich gas is prepared for power generation of the fuel cell; and dynamically adjusting the operation number of the independent power generation units according to the real-time power demand change. The technical problems that an existing distributed methanol power generation method depends on single parameter adjustment, is difficult to cope with the coupling effect of multiple factors, and is low in operation efficiency and poor in adaptability are solved, and the technical effect that the operation efficiency and adaptability of a methanol power generation system are improved by introducing a multi-index optimization algorithm and a bidirectional optimization feedback mechanism is achieved.