Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics
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Cited by:
- Hongwen Dou & Radu Zmeureanu, 2023. "Transfer Learning Prediction Performance of Chillers for Neural Network Models," Energies, MDPI, vol. 16(20), pages 1-16, October.
- Mateusz Malarczyk & Jules-Raymond Tapamo & Marcin Kaminski, 2022. "Application of Neural Data Processing in Autonomous Model Platform—A Complex Review of Solutions, Design and Implementation," Energies, MDPI, vol. 15(13), pages 1-22, June.
- Devi Munandar & Budi Nurani Ruchjana & Atje Setiawan Abdullah & Hilman Ferdinandus Pardede, 2023. "Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting," Mathematics, MDPI, vol. 11(13), pages 1-25, July.
- Yang, Yiran & Li, Gang & Luo, Tao & Al-Bahrani, Mohammed & Al-Ammar, Essam A. & Sillanpaa, Mika & Ali, Shafaqat & Leng, Xiujuan, 2023. "The innovative optimization techniques for forecasting the energy consumption of buildings using the shuffled frog leaping algorithm and different neural networks," Energy, Elsevier, vol. 268(C).
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Keywords
building characteristics; deep neural networks; hyper-parameter tuning; prediction models; energy consumption; heating and cooling loads;All these keywords.
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