A brief review of recent data mining applications in the energy industry

Data Mining has revolutionized the modern world and is rapidly transforming into a key ingredient for the successful discovery and extraction of relationships, hidden patterns and trends in the energy sector. The emergence and availability of Big Data in the energy industry adds to the prolific importance of Data Mining techniques as they provide feasible solutions for mining and exploiting information crucial for management decision making and achieving productivity gains in the energy industry. This succinct review paper seeks to outline the various Data Mining techniques that have recently been adopted for solving energy related problems in the industry. The evidence suggests that a variety of Data Mining techniques have been evaluated for solving energy related problems in the recent past, and these include cluster analysis, classification trees, neural networks, genetic algorithm, dynamic regressions, Bayesian models, support vector machines, k-nearest neighbours, and random forests. This paper finds evidence supportive of the claim that cluster analysis and classification trees are the most frequently adopted Data Mining techniques in the energy industry.