Optimal Task and Energy Scheduling in Dynamic Residential Scenarios

  • Francesco De Angelis
  • Matteo Boaro
  • Danilo Fuselli
  • Stefano Squartini
  • Francesco Piazza
  • Qinglai Wei
  • Ding Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7367)

Abstract

Smart homes of the future will include automation systems that will provide lower energy consumption costs and comfortable environments to end users. In this work we propose an algorithm, based on the “Mixed-Integer Linear Programming” paradigm, able to find the optimal task and energy scheduling in realistic residential scenarios, in order to reduce costs and satisfy the user requirements at the same time. Both the static and the dynamic case studies have been addressed on purpose and results obtained from computer simulations seem to confirm the effectiveness of the idea.

Keywords

Particle Swarm Optimization Smart Grid Task Schedule Smart Home Optimal Task 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Francesco De Angelis
    • 1
  • Matteo Boaro
    • 1
  • Danilo Fuselli
    • 1
  • Stefano Squartini
    • 1
  • Francesco Piazza
    • 1
  • Qinglai Wei
    • 2
  • Ding Wang
    • 2
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversitá Politecnica delle MarcheAnconaItaly
  2. 2.State Key Laboratory of Management and Control for Complex Systems, Institute of AutomationChinese Academy of SciencesBeijingChina

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