Short-Term Energy Forecasting... (symmetry 국제학술저널지, 2022.01) > 자유게시판

• 목록

논문명 

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

요약 

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. 

논문정보 

Citation: Phyo, P.-P.; Byun, Y.-C.; Park,

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

Symmetry 2022, 14, 160. https://doi.org/10.3390/sym14010160

Academic Editor: Alexander Zaslavski

Received: 5 November 2021

Accepted: 27 December 2021

Published: 14 January 2022

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.