Building Behavior Simulation by Means of Artificial Neural Network in Summer Conditions
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- Buratti, C. & Palladino, D. & Ricciardi, P., 2016. "Application of a new 13-value thermal comfort scale to moderate environments," Applied Energy, Elsevier, vol. 180(C), pages 859-866.
- Marek Dudzik, 2020. "Towards Characterization of Indoor Environment in Smart Buildings: Modelling PMV Index Using Neural Network with One Hidden Layer," Sustainability, MDPI, vol. 12(17), pages 1-37, August.
- Jiani Heng & Chen Wang & Xuejing Zhao & Liye Xiao, 2016. "Research and Application Based on Adaptive Boosting Strategy and Modified CGFPA Algorithm: A Case Study for Wind Speed Forecasting," Sustainability, MDPI, vol. 8(3), pages 1-25, March.
- Enescu, Diana, 2017. "A review of thermal comfort models and indicators for indoor environments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1353-1379.
- Domenico Palladino & Iole Nardi & Cinzia Buratti, 2020. "Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm," Energies, MDPI, vol. 13(17), pages 1-27, September.
- Ma, Nan & Aviv, Dorit & Guo, Hongshan & Braham, William W., 2021. "Measuring the right factors: A review of variables and models for thermal comfort and indoor air quality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
- Zini, Marco & Carcasci, Carlo, 2023. "Machine learning-based monitoring method for the electricity consumption of a healthcare facility in Italy," Energy, Elsevier, vol. 262(PB).
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Keywords
Artificial Neural Network (ANN); building envelope behaviour; unsteady simulations; cooling conditions;All these keywords.
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