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Optimal Battery Management with ADHDP in Smart Home Environments

  • Danilo Fuselli
  • Francesco De Angelis
  • Matteo Boaro
  • Derong Liu
  • Qinglai Wei
  • Stefano Squartini
  • Francesco Piazza
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7368)

Abstract

In this paper an optimal controller for battery management in smart home environments is presented in order to save costs and minimize energy waste. The considered scenario includes a load profile that must always be satisfied, a battery-system that is able to storage electrical energy, a photovoltaic (PV) panel, and the main grid that is used when it is necessary to satisfy the load requirements or charge the battery. The optimal controller design is based on a class of adaptive critic designs (ACDs) called action dependent heuristic dynamic programming (ADHDP). Results obtained with this scheme outperform the ones obtained by using the particle swarm optimization (PSO) method.

Keywords

Particle Swarm Optimization Action Network Particle Swarm Optimization Algorithm Optimal Controller Particle Swarm Optimization Method 
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

  • Danilo Fuselli
    • 1
  • Francesco De Angelis
    • 1
  • Matteo Boaro
    • 1
  • Derong Liu
    • 2
  • Qinglai Wei
    • 2
  • Stefano Squartini
    • 1
  • Francesco Piazza
    • 1
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversitá Politecnica delle MarcheAnconaItaly
  2. 2.State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijingChina

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