Development of a Decisional Procedure Based on Fuzzy Logic for the Energy Retrofitting of Buildings
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- Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2017. "Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach," Energy, Elsevier, vol. 118(C), pages 999-1017.
- Choi, Jun-Ki & Morrison, Drew & Hallinan, Kevin P. & Brecha, Robert J., 2014. "Economic and environmental impacts of community-based residential building energy efficiency investment," Energy, Elsevier, vol. 78(C), pages 877-886.
- García Kerdan, Iván & Raslan, Rokia & Ruyssevelt, Paul & Morillón Gálvez, David, 2017. "ExRET-Opt: An automated exergy/exergoeconomic simulation framework for building energy retrofit analysis and design optimisation," Applied Energy, Elsevier, vol. 192(C), pages 33-58.
- Aktacir, Mehmet Azmi & Büyükalaca, Orhan & YIlmaz, Tuncay, 2010. "A case study for influence of building thermal insulation on cooling load and air-conditioning system in the hot and humid regions," Applied Energy, Elsevier, vol. 87(2), pages 599-607, February.
- Wada, Kenichi & Akimoto, Keigo & Sano, Fuminori & Oda, Junichiro & Homma, Takashi, 2012. "Energy efficiency opportunities in the residential sector and their feasibility," Energy, Elsevier, vol. 48(1), pages 5-10.
- Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
- Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
- Soteris A. Kalogirou, 2006. "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 1(3), pages 201-216, July.
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Keywords
building design and architecture; energy retrofitting; energy efficiency in buildings; energy diagnosis; fuzzy logic;All these keywords.
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