Building Heat Demand Forecasting by Training a Common Machine Learning Model with Physics-Based Simulator
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References listed on IDEAS
- Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
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- Triebs, Merlin Sebastian & Tsatsaronis, George, 2022. "From heat demand to heat supply: How to obtain more accurate feed-in time series for district heating systems," Applied Energy, Elsevier, vol. 311(C).
- Alexander J. Bogensperger & Yann Fabel & Joachim Ferstl, 2022. "Accelerating Energy-Economic Simulation Models via Machine Learning-Based Emulation and Time Series Aggregation," Energies, MDPI, vol. 15(3), pages 1-42, February.
- Kalevi Piira & Julia Kantorovitch & Lotta Kannari & Jouko Piippo & Nam Vu Hoang, 2022. "Decision Support Tool to Enable Real-Time Data-Driven Building Energy Retrofitting Design," Energies, MDPI, vol. 15(15), pages 1-17, July.
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Keywords
building energy modelling; machine learning; artificial neural networks; demand response; short-term forecasting; simulation;All these keywords.
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