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Innovative framework for accurate and transparent forecasting of energy consumption: A fusion of feature selection and interpretable machine learning
,
Hassan Saadatmand,
Muhammad Ramzan,
Mobina Mousapour
Applied Energy, Volume: 366, Start page: 123314
Swansea University Author:
Hamid Eskandari
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© 2024 The Authors. This is an open access article under the CC BY license.
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DOI (Published version): 10.1016/j.apenergy.2024.123314
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 interpretab...
| Published in: | Applied Energy |
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| ISSN: | 0306-2619 |
| Published: |
Elsevier BV
2024
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa66476 |
| 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 framework's capability to provide accurate forecasts but also establishes it as a valuable tool for policymakers and energy analysts. |
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| Keywords: |
Energy consumption forecasting; Interpretable machine learning; Ensemble feature selection; Wrapper feature selection; Shapley analysis |
| College: |
Faculty of Humanities and Social Sciences |
| Start Page: |
123314 |