Accurate Prediction of Power Load Based on Model Combination
Electricity load forecasting involves numerous environmental and social factors.Therefore,designing an efficient and accurate prediction model has been an important task in the power industry.In this paper,a power load forecasting model based on a combination of traditional machine learning models is designed.The model starts from the feature level and adopts the feature ranking selection method of Wrapper and Gradient to filter and optimize the main features.In the experiments,the prediction results are cross-validated and compared with the K-fold timing based on the temporal expansion window splitting.The results show that the designed model can effectively predict the short-term time-series variation of power loads,and the prediction results are better than the traditional single machine learning model.The combination of hyperparameters obtained through the Bayesian hyperparameter tuning method can significantly improve the accuracy and generalization ability of the model.This indicates that the Bayesian hyperparameter tuning method can solve the model overfitting and underfitting problems to a certain extent,and improve the stability and reliability of the model.
short-term power load forecastingmachine learningBayesian hyperparameter optimizationtime series cross validationtime series prediction
NETL
NSTL
万方数据
