Journal
ALGORITHMS
Volume 15, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/a15060205
Keywords
combinatorial optimization problem; machine learning; supervised learning; reinforcement learning; game theory; refinery scheduling; steel-making; electric power system; wind power
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This review focuses on the modern machine learning (ML) approaches for combinatorial optimization problems (COPs) in the energy areas. Recent research on solving COPs using ML is sorted out based on different methods, and practical applications of ML in the energy areas are summarized. Challenges in this field are also analyzed.
Combinatorial optimization problems (COPs) are a class of NP-hard problems with great practical significance. Traditional approaches for COPs suffer from high computational time and reliance on expert knowledge, and machine learning (ML) methods, as powerful tools have been used to overcome these problems. In this review, the COPs in energy areas with a series of modern ML approaches, i.e., the interdisciplinary areas of COPs, ML and energy areas, are mainly investigated. Recent works on solving COPs using ML are sorted out firstly by methods which include supervised learning (SL), deep learning (DL), reinforcement learning (RL) and recently proposed game theoretic methods, and then problems where the timeline of the improvements for some fundamental COPs is the layout. Practical applications of ML methods in the energy areas, including the petroleum supply chain, steel-making, electric power system and wind power, are summarized for the first time, and challenges in this field are analyzed.
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