Prediction model of household appliance energy consumption based on machine learning
Lei Xiang, Tao Xie, Wei Xie
Abstract
Abstract This paper presents and discusses the prediction model of household appliance energy consumption. The paper discusses data filtering to remove non-predictive parameters, and used for model training. Five prediction models are trained with repeated cross validation and evaluated in the testing set: support vector machine (SVM), k nearest neighbor (KNN), random forest (RF), extreme random forest (ERF), and long short-term memory network (LSTM). Among them, the LSTM, which is a deep learning method, behaves the best performance. LSTM has the highest R 2 (0.97) and the lowest RMSE (21.36) in the testing set. The analysis results show that the deep learning method has advantages in the prediction of household appliance energy consumption.