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

  • Zunaira Nadeem
  • Nadeem Javaid
  • Asad Waqar Malik
  • Aqib Jamil
  • Itrat Fatima
  • Muhammad Usman Khalid
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 772)

Abstract

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Zunaira Nadeem
    • 1
  • Nadeem Javaid
    • 2
  • Asad Waqar Malik
    • 1
  • Aqib Jamil
    • 2
  • Itrat Fatima
    • 2
  • Muhammad Usman Khalid
    • 2
  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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