Affiliations: [a] Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska, Warsaw, Poland | [b] Institute of Systems Research, Polish Academy of Sciences, Newelska, Warsaw, Poland
Correspondence:
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Corresponding author. Tomasz Ząbkowski, Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska 159, Warsaw 02-776, Poland. Tel.: +48 22 593 7290; Fax: +48 22 593 7271; E-mail: tomasz_zabkowski@sggw.pl.
Abstract: Smart metering is a quite new topic that has grown in importance all over the world and it appears to be a remedy for rising prices of electricity. Forecasting electricity usage is an important task to provide intelligence to the smart gird. Accurate forecasting will enable a utility provider to plan the resources and also to take control actions to balance the electricity supply and demand. The customers will benefit from metering solutions through greater understanding of their own energy consumption and future projections, allowing them to better manage costs of their usage. In this proof of concept paper, our contribution is twofold: (1) we deal with short term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level what fits into the stream of Residential Power Load Forecasting (RPLF) methods; (2) we utilized a set of household behavioral data which significantly improved the forecasts accuracy.
Keywords: Smart metering, short term electricity forecasting, forecast accuracy, appliance recognition
