Research Area:  Machine Learning

Abstract:

The study presents a novel framework integrating feature selection (FS) and machine learning (ML) techniques to forecast inland national energy consumption (EC) in the United Kingdom across all energy sources. This innovative framework strategically combines three FS approaches with five interpretable ML models using Shapley Additive Explanations (SHAP), with the dual goal of enhancing accuracy and transparency in EC predictions. By meticulously selecting the most pertinent features from diverse features—including meteorological conditions, socioeconomic parameters, and historical consumption patterns of different primary fuels—the proposed framework enhances the robustness of the forecasting model. This is achieved through benchmarking three FS approaches: ensemble filter, wrapper, and a hybrid ensemble filter-wrapper. In addition, we introduce a novel ensemble filter FS, synthesizing outcomes from multiple base FS methods to make well-informed decisions about feature retention. Experimental results underscore the efficacy of integrating both wrapper and ensemble filter-wrapper FS approaches with interpretable ML models, ensuring the forecasting process remains comprehensible and interpretable while utilizing a manageable number of features (four to eight). In addition, experimental results indicate that different feature subsets are usually selected for each combined FS approach and ML model. This study not only demonstrates the frameworks capability to provide accurate forecasts but also establishes it as a valuable tool for policymakers and energy analysts.

Keywords:  

Author(s) Name:  Hamidreza Eskandar, Hassan Saadatmand, Muhammad Ramzan, Mobina Mousapour

Publisher name:  ScienceDirect

DOI:  10.1016/j.apenergy.2024.123314

Volume Information:  Volume 366, (2024)

Paper Link:   https://www.sciencedirect.com/science/article/pii/S0306261924006974