Enhancing hourly heat demand prediction through artificial neural networks: A national level case study
Meeting the goal of zero emissions in the energy sector by 2050 requires accurate prediction of energy consumption, which is increasingly important. However, conventional bottom-up model-based heat demand forecasting methods are not suitable for large-scale, high-resolution, and fast forecasting due...
Main Authors: | Meng Zhang, Michael-Allan Millar, Si Chen, Yaxing Ren, Zhibin Yu, James Yu |
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Format: | Article |
Language: | English |
Published: |
Elsevier
2024-01-01
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Series: | Energy and AI |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546823000873 |
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