Forecasting the Short-Term Energy Consumption Using Random Forests and Gradient Boosting

• Published in: 2021 20th RoEduNet Conference: Networking in Education and Research (RoEduNet) pp. 1 - 6
• Main Authors: Pop, Cristina Bianca, Chifu, Viorica Rozina, Cordea, Corina, Chifu, Emil Stefan, Barsan, Octav
• Format: Conference Proceeding
• Language: English
• Published: IEEE 04-11-2021
• Subjects: Boosting; Data models; Energy consumption; ensemble method; Forecasting; forecasting the energy consumption; gradient boosting; Machine learning algorithms; Predictive models; random forests; Training
• ISSN: 2247-5443
• DOI: 10.1109/RoEduNet54112.2021.9638276

This paper analyzes comparatively the performance of Random Forests and Gradient Boosting algorithms in the field of forecasting the energy consumption based on historical data. The two algorithms are applied in order to forecast the energy consumption individually, and then combined together by using a Weighted Average Ensemble Method. The comparison among the achieved experimental results proves that the Weighted Average Ensemble Method provides more accurate results than each of the two algorithms applied alone.