An Ensemble Framework for Short-Term Load Forecasting Based on TimesNet and TCN
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- Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
- Mingping Liu & Xihao Sun & Qingnian Wang & Suhui Deng, 2022. "Short-Term Load Forecasting Using EMD with Feature Selection and TCN-Based Deep Learning Model," Energies, MDPI, vol. 15(19), pages 1-22, September.
- Weide Li & Xuan Yang & Hao Li & Lili Su, 2017. "Hybrid Forecasting Approach Based on GRNN Neural Network and SVR Machine for Electricity Demand Forecasting," Energies, MDPI, vol. 10(1), pages 1-17, January.
- Jemma J. Makrygiorgou & Christos-Spyridon Karavas & Christos Dikaiakos & Ioannis P. Moraitis, 2023. "The Electricity Market in Greece: Current Status, Identified Challenges, and Arranged Reforms," Sustainability, MDPI, vol. 15(4), pages 1-40, February.
- Xiao, Liye & Shao, Wei & Liang, Tulu & Wang, Chen, 2016. "A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting," Applied Energy, Elsevier, vol. 167(C), pages 135-153.
- Weili Cai & Wenjuan Zhang & Xiaofeng Hu & Yingchao Liu, 2020. "A hybrid information model based on long short-term memory network for tool condition monitoring," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1497-1510, August.
- Hafeez, Ghulam & Alimgeer, Khurram Saleem & Khan, Imran, 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid," Applied Energy, Elsevier, vol. 269(C).
- Zhang, Jinliang & Wei, Yi-Ming & Li, Dezhi & Tan, Zhongfu & Zhou, Jianhua, 2018. "Short term electricity load forecasting using a hybrid model," Energy, Elsevier, vol. 158(C), pages 774-781.
- Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
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- Zain Ahmed & Mohsin Jamil & Ashraf Ali Khan, 2024. "Short-Term Campus Load Forecasting Using CNN-Based Encoder–Decoder Network with Attention," Energies, MDPI, vol. 17(17), pages 1-19, September.
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Keywords
short-term power load forecasting; multi-periodicity; TimesNet; temporal convolutional network;All these keywords.
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