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

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DOI: 10.4236/sn.2015.44014    3,154 Downloads   3,752 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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

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.

References

[1] Zhou, O., et al. (2012) Semantic Information Modeling for Emerging Applications in Smart Grid. Proceedings of the 2012 Ninth International Conference on Information Technology—New Generations, ITNG’12, IEEE Computer Society, Washington DC, 775-782. http://dx.doi.org/10.1109/ITNG.2012.150
[2] Aung, Z., Toukhy, M., Williams, J.R., Sanchez, A. and Herrero, S. (2012) Towards Accurate Electricity Load Forecasting in Smart Grids. The Fourth International Conference on Advances in Databases, Knowledge, and Data Application, DBKDA.
[3] Hernandez, L., Baladrón, C., Aguiar, J.M., Carro, B., Sanchez-Esguevillas, A.J. and Lloret, J. (2013) Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies, 6, 1385-1408.
http://dx.doi.org/10.3390/en6031385
[4] Lai, S.-H. and Hong, T. (2014) When One Size No Longer Fits All—Electric Load Forecasting with a Geographic Hierarchy. White Paper, SAS.
http://assets.fiercemarkets.com/public/sites/energy/reports/electricloadforecasting.pdf
[5] Day, P., et al. (2014) Residential Power Load Forecasting. Proceeding of the CSER 2014 Conference, Redondo Beach, March 2014, 457-464. http://dx.doi.org/10.1016/j.procs.2014.03.056
[6] Mikkola, J. and Lund, P.D. (2014) Models for Generating Place and Time Dependent Urban Energy Demand Profiles. Applied Energy, 130, 256-264. http://dx.doi.org/10.1016/j.apenergy.2014.05.039
[7] Ding, N., Besanger, Y., Wurtz, F., Antoine, G. and Deschamps, P. (2011) Time Series Method for Short-Term Load Forecasting Using Smart Metering in Distribution Systems. IEEE Trondheim Power Tech, Trondheim, 19-23 June 2011, 1-6. http://dx.doi.org/10.1109/ptc.2011.6019331
[8] Hong, T. and Shahidehpour, M. (2015) Load Forecasting Case Study. EISPC, U.S. Department of Energy.
[9] Hong, T. (2014) Energy Forecasting: Past, Present, and Future. FORESIGHT, Winter 2014, 43-48.
[10] Amjady, N., Keynia, F. and Zareipour, H. (2010) Short-Term Load Forecast of Microgrids by a New Bilevel Prediction Strategy. IEEE Transactions on Smart Grid, 1, 286-294.
http://dx.doi.org/10.1109/TSG.2010.2078842
[11] Ilic, S.I., Vukmirovic, S.M., Erdeljan, A.M. and Kulic, F.J. (2012) Hybrid Artificial Neural Network System for Short-Term Load Forecasting. Thermal Science, 16, 215-224.
http://dx.doi.org/10.2298/TSCI120130073I
[12] Feinberg, E.A., Fei, J., Hajagos, J.T. and Rossin, R.J. (2011) Smart Grid Software Applications for Distribution Network Load Forecasting. Proceedings of the First International Conference on Smart Grids, Green Communications and IT Energy-Aware Technologies, Venice, 22-27 May 2011.
[13] Hosking, G. and Zhang, N.S. (2013) Short-Term Forecasting on the Daily Load Curve for Residential Electricity Usage in the Smart Grid. IBM Research Report.
[14] Jain, A. and Jain, M.B. (2013) Fuzzy Modeling and Similarity Based Short Term Load Forecasting Using Swarm Intelligence—A Step towards Smart Grid. In: Bansal, J.C., et al., Eds., Proceedings of the Seventh International Conference on Bio-Inspired Computing: Theories and Applications, Springer India, New Delhi, 15-27.
[15] Serres, R. (2014) Short-Term Load Forecasting in New York State: NYISO Method and Support Vector Regression. Power System Analysis Project, Columbia University, New York.
[16] Charlton, N. and Singleton, C. (2014) A Refined Parametric Model for Short Term Load Forecasting. International Journal of Forecasting, 30, 364-368. http://dx.doi.org/10.1016/j.ijforecast.2013.07.003
[17] Hong, T. (2010) Short Term Electric Load Forecasting. PhD Dissertation, North Carolina State University, Raleigh.
[18] Hong, T., Wang, P. and White, L. (2015) Weather Station Selection for Electric Load Forecasting. International Journal of Forecasting, 31, 286-295. http://dx.doi.org/10.1016/j.ijforecat.2014.07.001
[19] Simmhan, Y., et al. (2011) An Informatics Approach to Demand Response Optimization in Smart Grids. Technical Report, USC.
[20] Microsoft Corp (2013) Smart Energy Reference Architecture V2.0. Technical Report.
[21] Oracle Corp (2010) Los Angeles Department of Water & Power Selects Oracle. Press Release.
[22] Luckham, D.C. (2007) A Brief Overview of the Concepts of CEP. Technical Report.
[23] Haben, S., Ward, J., Greetham, D.V., Singleton, C. and Grindrod, P. (2014) A New Error Measure for Forecasts of Household-Level, High Resolution Electrical Energy Consumption. International Journal of Forecasting, 30, 246-256. http://dx.doi.org/10.1016/j.ijforecast.2013.08.002
[24] Hu, Z.Y., Bao, Y.K., Xiong, T. and Chiong, R. (2015) Hybrid Filter—Wrapper Feature Selection for Short-Term Load Forecasting. Engineering Applications of Artificial Intelligence, 40, 17-27.
http://dx.doi.org/10.1016/j.engappai.2014.12.014

  
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