Potential of Model-Free Control for Demand-Side Management Considering Real-Time Pricing
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- Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
- Kalogirou, Soteris A. & Bojic, Milorad, 2000. "Artificial neural networks for the prediction of the energy consumption of a passive solar building," Energy, Elsevier, vol. 25(5), pages 479-491.
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- Suyang Zhou & Di He & Zhiyang Zhang & Zhi Wu & Wei Gu & Junjie Li & Zhe Li & Gaoxiang Wu, 2019. "A Data-Driven Scheduling Approach for Hydrogen Penetrated Energy System Using LSTM Network," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
- Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
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Keywords
data predictive control; neural network; energy management;All these keywords.
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