An overview of decision tree applied to power systems

Author(s): Chengxi Liua*, Zakir Hussain Rathera,b, Zhe Chena, Claus Leth Baka
Department of Energy Technology, Aalborg University, 9220 Aalborg Denmark
Innovation Centre, kk-electronic, a/s, 9220 Aalborg Denmark
International Journal of Smart Grid and Clean Energy, vol. 2, no. 3, October 2013: pp. 413-419
ISSN: 2315-4462
Digital Object Identifier: 10.12720/sgce.2.3.413-419

Abstract: The corrosive volume of available data in electric power systems motivates the adoption of data mining techniques in the emerging field of power system data analytics. The mainstream of data mining algorithm applied to power system, decision tree (DT), also named as classification and regression tree (CART), has gained increasing interests because of its high performance in terms of computational efficiency, uncertainty manageability, and interpretability. The fundamental knowledge of CART algorithm is introduced in this paper, followed by an overview of a variety of DT applications to power systems for better interfacing power systems with data analytics.

Keywords: Classification and regression tree, data mining, decision tree, power system data analytics

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