Neural network controller for Active Demand-Side Management with PV energy in the residential sector
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DOI: 10.1016/j.apenergy.2011.09.004
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- 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.
- 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.
- 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.
- 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.
- Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
- Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
- 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.
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Keywords
Demand-Side Management; Distributed energy; PV systems; Control system; Artificial Neural Network; Genetic algorithm;All these keywords.
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