Data analytics in the electricity sector – A quantitative and qualitative literature review

• Main Authors: Frederik vom Scheidt, Hana Medinová, Nicole Ludwig, Bent Richter, Philipp Staudt, Christof Weinhardt
• Format: Article
• Language: English
• Published: Elsevier 2020-08-01
• Series: Energy and AI
• Subjects: Data analytics; Electricity; Machine learning; Generation; Price; Transmission
• Online Access: http://www.sciencedirect.com/science/article/pii/S2666546820300094

The rapid transformation of the electricity sector increases both the opportunities and the need for Data Analytics. In recent years, various new methods and fields of application have been emerging. As research is growing and becoming more diverse and specialized, it is essential to integrate and structure the fragmented body of scientific work. We therefore conduct a systematic review of studies concerned with developing and applying Data Analytics methods in the context of the electricity value chain. First, we provide a quantitative high-level overview of the status quo of Data Analytics research, and show historical literature growth, leading countries in the field and the most intensive international collaborations. Then, we qualitatively review over 200 high-impact studies to present an in-depth analysis of the most prominent applications of Data Analytics in each of the electricity sector’s areas: generation, trading, transmission, distribution, and consumption. For each area, we review the state-of-the-art Data Analytics applications and methods. In addition, we discuss used data sets, feature selection methods, benchmark methods, evaluation metrics, and model complexity and run time. Summarizing the findings from the different areas, we identify best practices and what researchers in one area can learn from other areas. Finally, we highlight potential for future research.