Research On Short-term Power Load Forecasting Based On Machine Learning Algorithm

Power load forecasting is the basis for the development planning of the power sector and the prerequisite for ensuring stable operation of the power grid.With the complication of power consumption scenarios,higher requirements are imposed on power load forecasting.Accurate power load forecasting helps to maintain the balance of power supply and demand and improve the economic benefits of the power grid.Current power load forecasting methods lack adequate mining and utilization of time series information and information on various influencing factors.Machine learning algorithms with powerful data analysis capabilities provide a research basis for improving the efficiency and accuracy of power load forecasting.In view of the above problems,the specific research content of this paper is as follows:(1)Aiming at the problem that the abnormal data and attribute data in sample data are difficult to handle,this paper adopts a more effective data preprocessing scheme,including one-hot encoding,data standardization,vacant data filling and wavelet soft threshold denoising method.By preprocessing the historical load data,the prediction model can learn more accurate information,which paves the way for improving the prediction accuracy of the model.(2)Power load data has time-series attributes.Using the characteristics of long-term and short-term memory network(LSTM)to deal with time-series problems,a LSTM prediction model is constructed.The maximum information coefficient is used to calculate the factors that have a greater correlation with the power load as input variables.In order to avoid the gradient vanishing problem caused by the derivation and convergence of the activation function,softsign activation function is introduced to optimize the LSTM model,and the regularization method is used to prevent the model from over fitting.Finally,using real sample data for experiments,in the case of fewer input features,the above LSTM model has stronger prediction performance than other prediction methods.(3)Aiming at the difficulty of selecting the input features of the model during power load forecasting,based on the extreme gradient boosting(XGBoost)algorithm,an XGBoost forecasting model combining historical load and real-time influencing factors is constructed.The gradient lifting decision tree algorithm and the maximum information coefficient are used to analyze the different features,and the main features are reasonably selected as the input variables of the model,which improves the prediction accuracy of the model and reduces the calculation complexity.At the same time,in order to further improve the prediction performance of the model,the improved particle swarm optimization(IPSO)algorithm is introduced to optimize the super parameters of the model,aiming at the problems of many parameters and difficult optimization of XGBoost algorithm.Finally,through examples of power load forecasting with different time spans,compared with other forecasting methods,IPSO-XGBoost can effectively improve the accuracy of short-term power load forecasting.