Towards Real-Time Opportunistic Scheduling of the Home Appliances Using Evolutionary Techniques

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 772)


The tremendous evolution of the technology has empowered the energy consumers to receive real-time information regarding electricity consumption prices with the help of two way communication between the main grid and the smart meter. We have proposed evolutionary optimization techniques such as; genetic algorithm (GA) and teaching-learning base algorithm (TLBO) in this paper. The aforementioned algorithms are exploited to find out an optimal schedule for every appliance based on real-time pricing (RTP) signal. It enables the real-time automation of smart home appliances considering the economic criteria of each smart home. Our scheduling strategy shifts the extra load exceeding the threshold limit to the hours where the electricity pricing is low. In this way, we can reduce electricity cost while considering the user comfort by reducing delay and peak to average ratio (PAR).


Home Appliances Reduce Electricity Costs User Comfort Real-time Pricing (RTP) Unscheduled Cost 
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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer Science (SEECS)National University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.COMSATS Institute of Information TechnologyIslamabadPakistan

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