Research On Power Load Forecasting And Economic Dispatch Strategy Based On Power Consumption Data Analysis

With the development of the smart grid era,people are increasingly dependent on electric energy.The amount of data in the power system on the user side is growing rapidly,and the importance of electric energy is gradually increasing.This paper aims to maximize the benefits of both supply and demand in the power system by conducting research from two aspects:improving the accuracy of short-term load forecasting and optimizing the economic dispatch of the power system.This paper analyzes power consumption data and finds that the power demand side data is affected by multiple factors.Traditional load forecasting methods lack the processing of data randomness and cannot accurately predict the power consumption data.Therefore,based on the analysis of electricity consumption data,this paper proposes an improved hybrid neural network load forecasting model.Secondly,based on the prediction results of the improved model,we established a short-term economic dispatch model.The model takes the minimization of power system generation costs as the objective function and considers unit startup and shutdown as constraints to achieve optimal economic dispatch.In this paper,we primarily use neural network algorithms for short-term load forecasting.Neural networks have strong modeling ability for time series data,which can effectively reduce the dimensions of data required for short-term load forecasting models and mine the value of load data.To improve accuracy,this paper adds meteorological factors to the input data and clusters the meteorological data using the K-means clustering algorithm.Secondly,in order to improve the prediction accuracy of the neural network model,this paper compares the fire hawk optimization algorithm(FHO)and particle swarm optimization(PSO)and other optimization algorithms,and then finds that FHO optimization algorithm has higher search speed and optimization quality than other optimization algorithms.Therefore,an improved algorithm combining K-means clustering algorithm,FHO optimization algorithm and neural network is proposed to improve the accuracy of the model by optimizing the input data,optimizing the number of hidden layer neurons,batch size and other hyperparameter,and by comparing examples with other traditional methods,it proves that it has higher accuracy and applicability,The mean square error(MSE)of the improved recurrent neural network(RNN)is reduced by 0.001,the mean square error of the improved short-term memory neural network(LSTM)is reduced by 0.0027,and the root mean square error of the improved gate neural network(GRU)is reduced by 0.003.Finally,based on the analysis of the benefits of both supply and demand in the power market,this paper establishes a unit commitment optimization model for the supply side of the power system.The model takes into account the conditions such as unit startup and shutdown as constraints and the results of load forecasting as boundary conditions to reduce the operating costs of the supply side of the power system.Accurate load forecasting results can also provide a more scientific and effective basis for the demand side response mechanism.Therefore,this paper proposes relevant economic dispatch optimization suggestions to achieve dynamic balance between supply and demand in the power system and maximize comprehensive benefits.