Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area
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- Hossein Bakhtiari & Jan Akander & Mathias Cehlin & Abolfazl Hayati, 2020. "On the Performance of Night Ventilation in a Historic Office Building in Nordic Climate," Energies, MDPI, vol. 13(16), pages 1-26, August.
- Singh, Manoj Kumar & Attia, Shady & Mahapatra, Sadhan & Teller, Jacques, 2016. "Assessment of thermal comfort in existing pre-1945 residential building stock," Energy, Elsevier, vol. 98(C), pages 122-134.
- Groscurth, H.-M. & Kress, K.-P., 1998. "Fuzzy data compression for energy optimization models," Energy, Elsevier, vol. 23(1), pages 1-9.
- Lee-Yong Sung & Jonghoon Ahn, 2020. "Comparative Analyses of Energy Efficiency between on-Demand and Predictive Controls for Buildings’ Indoor Thermal Environment," Energies, MDPI, vol. 13(5), pages 1-15, March.
- Ren, Zhengen & Chen, Dong, 2018. "Modelling study of the impact of thermal comfort criteria on housing energy use in Australia," Applied Energy, Elsevier, vol. 210(C), pages 152-166.
- Ahn, Jonghoon & Cho, Soolyeon, 2017. "Anti-logic or common sense that can hinder machine’s energy performance: Energy and comfort control models based on artificial intelligence responding to abnormal indoor environments," Applied Energy, Elsevier, vol. 204(C), pages 117-130.
- Branko Simanic & Birgitta Nordquist & Hans Bagge & Dennis Johansson, 2020. "Influence of User-Related Parameters on Calculated Energy Use in Low-Energy School Buildings," Energies, MDPI, vol. 13(11), pages 1-14, June.
- 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.
- Ahn, Jonghoon & Cho, Soolyeon & Chung, Dae Hun, 2017. "Analysis of energy and control efficiencies of fuzzy logic and artificial neural network technologies in the heating energy supply system responding to the changes of user demands," Applied Energy, Elsevier, vol. 190(C), pages 222-231.
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- Jonghoon Ahn, 2022. "A Network-Based Strategy to Increase the Sustainability of Building Supply Air Systems Responding to Unexpected Temperature Patterns," Sustainability, MDPI, vol. 14(22), pages 1-13, November.
- Sung Hoon Yoon & Jonghoon Ahn, 2020. "Comparative Analysis of Energy Use and Human Comfort by an Intelligent Control Model at the Change of Season," Energies, MDPI, vol. 13(22), pages 1-15, November.
- Jonghoon Ahn, 2021. "Abatement of the Increases in Cooling Energy Use during a Period of Intense Heat by a Network-Based Adaptive Controller," Sustainability, MDPI, vol. 13(3), pages 1-17, January.
- Jonghoon Ahn, 2020. "Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season," Sustainability, MDPI, vol. 12(22), pages 1-14, November.
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Keywords
building space; thermal comfort; energy use; fuzzy inference system; artificial neural network; cold weather;All these keywords.
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