Abstract
Smart homes form one of the major components leveraging demand response within the smart grid paradigm. Flexible pricing policies along with the capability of scheduling power among many homes form the crux of a wide variety of smart home power management controllers. However leveraging power scheduling for smart homes while keeping user costs minimal is a challenging proposition and involves complex multistage, stochastic, non-linear optimization techniques. For ease of computation, heuristic algorithms can be employed that require consumer load corresponding to smart homes which are not available a priori. The efficiency of power scheduling heuristics, however depend on the accuracy of the consumer loads forecasted. In this paper, we focus on developing a technique that can efficiently forecast consumer loads and thereafter the predicted load is fed to a GA heuristic based power scheduling algorithm for smart homes. Detailed procedure for the aforementioned forecasting has been presented and the results obtained are analyzed.
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Acknowledgments
This work has been carried out using the facilities prevailing at the Smart Grid Analytics Group at National Institute of Technology, Berhampur, India. The authors gratefully acknowledge the facilities provided.
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Behera, S., Pattnaik, B.S., Reza, M., Roy, D.S. (2016). Predicting Consumer Loads for Improved Power Scheduling in Smart Homes. In: Behera, H., Mohapatra, D. (eds) Computational Intelligence in Data Mining—Volume 2. Advances in Intelligent Systems and Computing, vol 411. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2731-1_44
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DOI: https://doi.org/10.1007/978-81-322-2731-1_44
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