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Algorithms 2016, 9(4), 80; doi:10.3390/a9040080

Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior

School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
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Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Received: 12 October 2016 / Revised: 16 November 2016 / Accepted: 17 November 2016 / Published: 23 November 2016
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Abstract

The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers. View Full-Text
Keywords: smart meter; clustering algorithm; load forecasting; OS-ELM; electricity behavior analysis smart meter; clustering algorithm; load forecasting; OS-ELM; electricity behavior analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, Y.; Guo, P.; Li, X. Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior. Algorithms 2016, 9, 80.

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