Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses
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- Manoj Manivannan & Behzad Najafi & Fabio Rinaldi, 2017. "Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics," Energies, MDPI, vol. 10(11), pages 1-17, November.
- Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
- Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
- Zixu Liu & Xiaojun Zeng & Fanlin Meng, 2018. "An Integration Mechanism between Demand and Supply Side Management of Electricity Markets," Energies, MDPI, vol. 11(12), pages 1-23, November.
- Sha, Huajing & Xu, Peng & Lin, Meishun & Peng, Chen & Dou, Qiang, 2021. "Development of a multi-granularity energy forecasting toolkit for demand response baseline calculation," Applied Energy, Elsevier, vol. 289(C).
- Tania Cerquitelli & Giovanni Malnati & Daniele Apiletti, 2019. "Exploiting Scalable Machine-Learning Distributed Frameworks to Forecast Power Consumption of Buildings," Energies, MDPI, vol. 12(15), pages 1-18, July.
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- Martin Stöckl & Johannes Idda & Volker Selleneit & Uwe Holzhammer, 2023. "Flexible Operation to Reduce Greenhouse Gas Emissions along the Cold Chain for Chilling, Storage, and Transportation—A Case Study for Dairy Products," Sustainability, MDPI, vol. 15(21), pages 1-27, November.
- Ali Kaboli & Farzad Dadras Javan & Italo Aldo Campodonico Avendano & Behzad Najafi & Luigi Pietro Maria Colombo & Sara Perotti & Fabio Rinaldi, 2024. "Just-in-Time Morning Ramp-Up Implementation in Warehouses Enabled by Machine Learning-Based Predictive Modelling: Estimation of Achievable Energy Saving through Simulation," Energies, MDPI, vol. 17(17), pages 1-18, September.
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Keywords
load forecasting; warehouse buildings; machine learning; flexibility in buildings; demand response; multi-layer perceptron;All these keywords.
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