Artificial Neural Network-Based Residential Energy Consumption Prediction Models Considering Residential Building Information and User Features in South Korea
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- Dongjun Suh & Seongju Chang, 2014. "A Heuristic Rule-Based Passive Design Decision Model for Reducing Heating Energy Consumption of Korean Apartment Buildings," Energies, MDPI, vol. 7(11), pages 1-33, October.
- Pan, Dongmei & Chan, Mingyin & Deng, Shiming & Lin, Zhongping, 2012. "The effects of external wall insulation thickness on annual cooling and heating energy uses under different climates," Applied Energy, Elsevier, vol. 97(C), pages 313-318.
- Seunghui Lee & Sungwon Jung & Jaewook Lee, 2019. "Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea," Energies, MDPI, vol. 12(4), pages 1-18, February.
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- Elsa Chaerun Nisa & Yean-Der Kuan, 2021. "Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
- Elsa Chaerun Nisa & Yean-Der Kuan & Chin-Chang Lai, 2021. "Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining," Energies, MDPI, vol. 14(20), pages 1-14, October.
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Keywords
artificial neural network; residential energy; user feature; residential building information;All these keywords.
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