Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression

Symmetry

Namje Park¹  Yung-Cheol Byun²  Pyae-Pyae Phyo² 

[1] Department of Computer Education, Teachers College, Jeju National University, 61 Iljudong-ro, Jeju-si 63294, Korea;Department of Computer Engineering, Jeju National University, Jeju-si 63243, Korea;

关键词: energy consumption;    energy forecasting;    extra trees regressor;    ensemble voting regressor;    forecasting accuracy;    K neighbors regressor;   

DOI  :  10.3390/sym14010160

来源: DOAJ

【 摘 要 】

Meeting the required amount of energy between supply and demand is indispensable for energy manufacturers. Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system. This paper firstly compares multiple machine learning (ML) regressors during the training process. Five best ML algorithms, such as extra trees regressor (ETR), random forest regressor (RFR), light gradient boosting machine (LGBM), gradient boosting regressor (GBR), and K neighbors regressor (KNN) are trained to build our proposed voting regressor (VR) model. Final predictions are performed using the proposed ensemble VR and compared with five selected ML benchmark models. Statistical autoregressive moving average (ARIMA) is also compared with the proposed model to reveal results. For the experiments, usage energy and weather data are gathered from four regions of Jeju Island. Error measurements, including mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) are computed to evaluate the forecasting performance. Our proposed model outperforms six baseline models in terms of the result comparison, giving a minimum MAPE of 0.845% on the whole test set. This improved performance shows that our approach is promising for symmetrical forecasting using time series energy data in the power system sector.

【 授权许可】

Unknown