Load forecasting of district heating system based on Informer
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DOI: 10.1016/j.energy.2022.124179
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- Jiang, Yuqi & Gao, Tianlu & Dai, Yuxin & Si, Ruiqi & Hao, Jun & Zhang, Jun & Gao, David Wenzhong, 2022. "Very short-term residential load forecasting based on deep-autoformer," Applied Energy, Elsevier, vol. 328(C).
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- Ze Wu & Feifan Pan & Dandan Li & Hao He & Tiancheng Zhang & Shuyun Yang, 2022. "Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
- Li, Tailu & Zhang, Yao & Wang, Jingyi & Jin, Fengyun & Gao, Ruizhao, 2024. "Techno-economic and environmental performance of a novel thermal station characterized by electric power generation recovery as by-product," Renewable Energy, Elsevier, vol. 221(C).
- Liu, Jingxuan & Zang, Haixiang & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A Transformer-based multimodal-learning framework using sky images for ultra-short-term solar irradiance forecasting," Applied Energy, Elsevier, vol. 342(C).
- Runge, Jason & Saloux, Etienne, 2023. "A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system," Energy, Elsevier, vol. 269(C).
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Keywords
District heating system; Heating load forecasting; Informer; Relative position encoding algorithm;All these keywords.
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