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