Abstract
The behavioral patterns identification is very important for time series analysis of energy consumption to assist planning activities and decision making, as well to seek improvements in service quality and financial benefits. In this paper we used a methodology based on data mining tools, including cluster analysis and time series representation. The Time Series Knowledge Mining [1] was adapted to the treatment of consumption electricity series. Results are shown in a case study with hourly consumption measurements of eight power substations.
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de Queiroz, A.C.S., Costa, J.A.F. (2012). Behavior Pattern Recognition in Electric Power Consumption Series Using Data Mining Tools. In: Yin, H., Costa, J.A.F., Barreto, G. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2012. IDEAL 2012. Lecture Notes in Computer Science, vol 7435. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32639-4_64
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DOI: https://doi.org/10.1007/978-3-642-32639-4_64
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