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Neural network controller for Active Demand-Side Management with PV energy in the residential sector

Author

Listed:
  • Matallanas, E.
  • Castillo-Cagigal, M.
  • Gutiérrez, A.
  • Monasterio-Huelin, F.
  • Caamaño-Martín, E.
  • Masa, D.
  • Jiménez-Leube, J.

Abstract

In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation.

Suggested Citation

  • Matallanas, E. & Castillo-Cagigal, M. & Gutiérrez, A. & Monasterio-Huelin, F. & Caamaño-Martín, E. & Masa, D. & Jiménez-Leube, J., 2012. "Neural network controller for Active Demand-Side Management with PV energy in the residential sector," Applied Energy, Elsevier, vol. 91(1), pages 90-97.
    • Handle: RePEc:eee:appene:v:91:y:2012:i:1:p:90-97
    DOI: 10.1016/j.apenergy.2011.09.004
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    References listed on IDEAS

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    1. Chaabene, Maher & Ammar, Mohsen Ben & Elhajjaji, Ahmed, 2007. "Fuzzy approach for optimal energy-management of a domestic photovoltaic panel," Applied Energy, Elsevier, vol. 84(10), pages 992-1001, October.
    2. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    3. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    4. Middelberg, Arno & Zhang, Jiangfeng & Xia, Xiaohua, 2009. "An optimal control model for load shifting - With application in the energy management of a colliery," Applied Energy, Elsevier, vol. 86(7-8), pages 1266-1273, July.
    5. Fadare, D.A., 2009. "Modelling of solar energy potential in Nigeria using an artificial neural network model," Applied Energy, Elsevier, vol. 86(9), pages 1410-1422, September.
    6. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    7. Papagiannis, G. & Dagoumas, A. & Lettas, N. & Dokopoulos, P., 2008. "Economic and environmental impacts from the implementation of an intelligent demand side management system at the European level," Energy Policy, Elsevier, vol. 36(1), pages 163-180, January.
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