Markovian Models for Electrical Load Prediction in Smart Buildings
Developing energy consumption models for smart buildings is important for studying demand response, home energy management, and distribution network simulation. In this work, we develop parsimonious Markovian models of smart buildings for different periods in a day for predicting electricity consumption. To develop these models, we collect two data sets with widely different load profiles over a period of seven months and one year, respectively. We validate the accuracy of our models for load prediction and compare our results with neural networks.
KeywordsSmart Grid Load Prediction Markov Processes
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- 1.Keshav, S., Rosenberg, C.: Direct adaptive control of electricity demand. Technical Report CS-2010-17, University of Waterloo (September 2010)Google Scholar
- 3.Asrari, A., Javan, D.S., Javidi, M.H., Monfared, M.: Application of gray-fuzzy-markov chain method for day-ahead electric load forecasting. Przeglad Elektrotechniczny-Electrical Review 2012(3), 228–237 (2012)Google Scholar
- 6.Hayati, M., Shirvany, Y.: Artificial neural network approach for short term load forecasting for illam region. International Journal of Electrical, Computer, and System Engineering 1(2), 121–125 (2007)Google Scholar
- 7.Ardakanian, O., Keshav, S., Rosenberg, C.: Markovian models for home electricity consumption. In: Proc. ACM SIGCOMM Green Networking Workshop (2011)Google Scholar
- 8.Neural Network Toolbox, http://www.mathworks.com/products/neural-network/