Energy conservation in buildings through efficient A/C control using neural networks
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- Xu, Xiaoqi & Culligan, Patricia J. & Taylor, John E., 2014. "Energy Saving Alignment Strategy: Achieving energy efficiency in urban buildings by matching occupant temperature preferences with a building’s indoor thermal environment," Applied Energy, Elsevier, vol. 123(C), pages 209-219.
- Antanasijević, Davor & Pocajt, Viktor & Ristić, Mirjana & Perić-Grujić, Aleksandra, 2015. "Modeling of energy consumption and related GHG (greenhouse gas) intensity and emissions in Europe using general regression neural networks," Energy, Elsevier, vol. 84(C), pages 816-824.
- K A H Kobbacy & S Vadera & M H Rasmy, 2007. "AI and OR in management of operations: history and trends," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(1), pages 10-28, January.
- Pang, Zhihong & Chen, Yan & Zhang, Jian & O'Neill, Zheng & Cheng, Hwakong & Dong, Bing, 2021. "How much HVAC energy could be saved from the occupant-centric smart home thermostat: A nationwide simulation study," Applied Energy, Elsevier, vol. 283(C).
- Du, Zhimin & Jin, Xinqiao & Yang, Yunyu, 2009. "Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network," Applied Energy, Elsevier, vol. 86(9), pages 1624-1631, September.
- Kusiak, Andrew & Xu, Guanglin & Tang, Fan, 2011. "Optimization of an HVAC system with a strength multi-objective particle-swarm algorithm," Energy, Elsevier, vol. 36(10), pages 5935-5943.
- 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.
- Yan, Huaxia & Pan, Yan & Li, Zhao & Deng, Shiming, 2018. "Further development of a thermal comfort based fuzzy logic controller for a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 219(C), pages 312-324.
- Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
- Kazmi, Hussain & Suykens, Johan & Balint, Attila & Driesen, Johan, 2019. "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads," Applied Energy, Elsevier, vol. 238(C), pages 1022-1035.
- Yang, Lei & Nagy, Zoltan & Goffin, Philippe & Schlueter, Arno, 2015. "Reinforcement learning for optimal control of low exergy buildings," Applied Energy, Elsevier, vol. 156(C), pages 577-586.
- Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2014. "A novel dynamic modeling approach for predicting building energy performance," Applied Energy, Elsevier, vol. 114(C), pages 91-103.
- Kusiak, Andrew & Xu, Guanglin, 2012. "Modeling and optimization of HVAC systems using a dynamic neural network," Energy, Elsevier, vol. 42(1), pages 241-250.
- Ghoroghi, Ali & Petri, Ioan & Rezgui, Yacine & Alzahrani, Ateyah, 2023. "A deep learning approach to predict and optimise energy in fish processing industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
- Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
- Goopyo Hong & Byungseon Sean Kim, 2018. "Development of a Data-Driven Predictive Model of Supply Air Temperature in an Air-Handling Unit for Conserving Energy," Energies, MDPI, vol. 11(2), pages 1-16, February.
- Jin Woo Moon & Sung Kwon Jung & Yong Oh Lee & Sangsun Choi, 2015. "Prediction Performance of an Artificial Neural Network Model for the Amount of Cooling Energy Consumption in Hotel Rooms," Energies, MDPI, vol. 8(8), pages 1-18, August.
- Michailidis, Iakovos T. & Schild, Thomas & Sangi, Roozbeh & Michailidis, Panagiotis & Korkas, Christos & Fütterer, Johannes & Müller, Dirk & Kosmatopoulos, Elias B., 2018. "Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study," Applied Energy, Elsevier, vol. 211(C), pages 113-125.
- Jin Woo Moon & Kyungjae Kim & Hyunsuk Min, 2015. "ANN-Based Prediction and Optimization of Cooling System in Hotel Rooms," Energies, MDPI, vol. 8(10), pages 1-21, September.
- Tzivanidis, C. & Antonopoulos, K.A. & Gioti, F., 2011. "Numerical simulation of cooling energy consumption in connection with thermostat operation mode and comfort requirements for the Athens buildings," Applied Energy, Elsevier, vol. 88(8), pages 2871-2884, August.
- Kusiak, Andrew & Li, Mingyang, 2010. "Cooling output optimization of an air handling unit," Applied Energy, Elsevier, vol. 87(3), pages 901-909, March.
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Keywords
Neural networks Energy conservation Air conditioning Control General regression Building simulation;Statistics
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