Improved Short Term Energy Load Forecasting Using Web-Based Social Networks

Mehmed Kantardzic; Haris Gavranovic; Nedim Gavranovic; Izudin Dzafic; Hanqing Hu

doi:10.4236/sn.2015.44014

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Social Networking > Vol.4 No.4, October 2015

Improved Short Term Energy Load Forecasting Using Web-Based Social Networks ()

Mehmed Kantardzic¹, Haris Gavranovic², Nedim Gavranovic³, Izudin Dzafic², Hanqing Hu¹

¹J. B. Speed School of Engineering, University of Louisville, Louisville, KY, USA.
²Faculty of Engineering and Sciences, International University of Sarajevo, Sarajevo, Bosnia and Herzegovina.
³College of Economics, American University in Bosnia and Herzegovina, Sarajevo, Bosnia and Herzegovina.

DOI: 10.4236/sn.2015.44014 PDF HTML XML 3,337 Downloads 3,990 Views Citations

Abstract

In this article, we are initiating the hypothesis that improvements in short term energy load forecasting may rely on inclusion of data from new information sources generated outside the power grid and weather related systems. Other relevant domains of data include scheduled activities on a grid, large events and conventions in the area, equipment duty cycle schedule, data from call centers, real-time traffic, Facebook, Twitter, and other social networks feeds, and variety of city or region websites. All these distributed data sources pose information collection, integration and analysis challenges. Our approach is concentrated on complex non-cyclic events detection where detected events have a human crowd magnitude that is influencing power requirements. The proposed methodology deals with computation, transformation, modeling, and patterns detection over large volumes of partially ordered, internet based streaming multimedia signals or text messages. We are claiming that traditional approaches can be complemented and enhanced by new streaming data inclusion and analyses, where complex event detection combined with Webbased technologies improves short term load forecasting. Some preliminary experimental results, using Gowalla social network dataset, confirmed our hypothesis as a proof-of-concept, and they paved the way for further improvements by giving new dimensions of short term load forecasting process in a smart grid.

Keywords

Short Term Energy Load Forecasting, Smart Grid, Social Networks, Event Detection

Share and Cite:

Kantardzic, M. , Gavranovic, H. , Gavranovic, N. , Dzafic, I. and Hu, H. (2015) Improved Short Term Energy Load Forecasting Using Web-Based Social Networks. Social Networking, 4, 119-131. doi: 10.4236/sn.2015.44014.

Conflicts of Interest

The authors declare no conflicts of interest.

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