Ensemble Machine Learning Modelling for Medium to Long Term Energy Consumption Forecasting
Main Article Content
Abstract
Electricity demand forecasting is an important research area that is gaining popularity among researchers these days. Forecasting energy consumption is a critical task as it affects the overall functioning of the power industry. There are many types of research already done in the field of short-term energy forecasting, but there is a scarcity of researches in medium to long-term energy forecasting. This paper focuses on medium to long-term energy forecasting using machine learning and the ensemble approach. The machine learning methods include Linear Regression (LR), Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Gradient Boosting Regressor (GBR). For the comparative analysis the performance metric selected are R2, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE)..
Downloads
Metrics
Article Details
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.