Markovian models for electrical load prediction in smart buildings

  • Authors:
  • Muhammad Kumail Haider;Asad Khalid Ismail;Ihsan Ayyub Qazi

  • Affiliations:
  • LUMS School of Science and Engineering, Lahore, Pakistan;LUMS School of Science and Engineering, Lahore, Pakistan;LUMS School of Science and Engineering, Lahore, Pakistan

  • Venue:
  • ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
  • Year:
  • 2012

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Abstract

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.