Real-time prediction of SO2 emission concentration under wide range of variable loads by convolution-LSTM VE-transformer
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DOI: 10.1016/j.energy.2023.126781
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Cited by:
- Wang, Yingnan & Chen, Xu & Zhao, Chunhui, 2024. "A data-driven soft sensor model for coal-fired boiler SO2 concentration prediction with non-stationary characteristic," Energy, Elsevier, vol. 300(C).
- Feng, Zhanyu & Zhang, Jian & Jiang, Han & Yao, Xuejian & Qian, Yu & Zhang, Haiyan, 2024. "Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework," Energy, Elsevier, vol. 302(C).
- Wang, Jianguo & Han, Lincheng & Zhang, Xiuyu & Wang, Yingzhou & Zhang, Shude, 2023. "Electrical load forecasting based on variable T-distribution and dual attention mechanism," Energy, Elsevier, vol. 283(C).
- Xiang, Ling & Fu, Xiaomengting & Yao, Qingtao & Zhu, Guopeng & Hu, Aijun, 2024. "A novel model for ultra-short term wind power prediction based on Vision Transformer," Energy, Elsevier, vol. 294(C).
- Gao, Wei & Liu, Ming & Xin, Haozhe & Zhao, Yongliang & Wang, Chaoyang & Yan, Junjie, 2024. "Control strategy optimization for wet flue gas desulfurization system during load cycling dynamic processes: Energy saving and environmental impact," Energy, Elsevier, vol. 303(C).
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Keywords
Vision-expansion self-attention; Convolution-LSTM; Dynamic model; Convolution-LSTM VE-Transformer; SO2 emission concentration;All these keywords.
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