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Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms

Department of Informatics, Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Nowoursynowska 159, 02-787 Warsaw, Poland
Author to whom correspondence should be addressed.
Energies 2017, 10(10), 1547;
Received: 21 August 2017 / Revised: 28 September 2017 / Accepted: 2 October 2017 / Published: 8 October 2017
(This article belongs to the Special Issue From Smart Metering to Demand Side Management)
Forecasting of electricity demand has become one of the most important areas of research in the electric power industry, as it is a critical component of cost-efficient power system management and planning. In this context, accurate and robust load forecasting is supposed to play a key role in reducing generation costs, and deals with the reliability of the power system. However, due to demand peaks in the power system, forecasts are inaccurate and prone to high numbers of errors. In this paper, our contributions comprise a proposed data-mining scheme for demand modeling through peak detection, as well as the use of this information to feed the forecasting system. For this purpose, we have taken a different approach from that of time series forecasting, representing it as a two-stage pattern recognition problem. We have developed a peak classification model followed by a forecasting model to estimate an aggregated demand volume. We have utilized a set of machine learning algorithms to benefit from both accurate detection of the peaks and precise forecasts, as applied to the Polish power system. The key finding is that the algorithms can detect 96.3% of electricity peaks (load value equal to or above the 99th percentile of the load distribution) and deliver accurate forecasts, with mean absolute percentage error (MAPE) of 3.10% and resistant mean absolute percentage error (r-MAPE) of 2.70% for the 24 h forecasting horizon. View Full-Text
Keywords: machine learning; electricity forecasting; demand modeling; peak identification machine learning; electricity forecasting; demand modeling; peak identification
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Gajowniczek, K.; Ząbkowski, T. Two-Stage Electricity Demand Modeling Using Machine Learning Algorithms. Energies 2017, 10, 1547.

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