Forecasting Energy Prices Using Machine Learning Algorithms: A Comparative Analysis

Frédéric Mirindi () and Derrick Mirindi
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Frédéric Mirindi: University of Manitoba
Derrick Mirindi: Morgan State University

A chapter in Machine Learning Technologies on Energy Economics and Finance, 2025, pp 135-146 from Springer

Abstract: Abstract Accurate forecasting of energy prices is crucial for effective decision-making in the energy sector. Traditional forecasting methods often struggle to capture the complex and dynamic nature of energy markets. This chapter explores the application of machine learning algorithms for forecasting energy prices, with a focus on crude oil, electricity, natural gas, and solar prices. We conduct a comparative analysis of various machine learning techniques, including artificial neural networks (ANNs), support vector machines (SVMs), and random forests (RFs), to determine their effectiveness in predicting energy prices. Our findings reveal that machine learning algorithms outperform traditional forecasting methods, with ANN and SVM exhibiting the highest accuracy. We also discuss the role of renewable energy technologies (RETs) in shaping energy economics and finance, highlighting their potential to reduce energy costs and increase revenue for economic growth. This research contributes to the advancement of energy systems and provides valuable insights for policymakers, financial managers, and stakeholders in the energy sector.

Keywords: Energy prices; Machine learning; Forecasting; Renewable energy technologies; Energy economics; Energy finance; Q41; Q42; Q47; C45; C53 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-94862-6_6

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DOI: 10.1007/978-3-031-94862-6_6

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