A Prosumer Power Prediction Method Based on Dynamic Segmented Curve Matching and Trend Feature Perception
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- Li, Kangping & Wang, Fei & Mi, Zengqiang & Fotuhi-Firuzabad, Mahmoud & Duić, Neven & Wang, Tieqiang, 2019. "Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
- Alipour, Mohammadali & Aghaei, Jamshid & Norouzi, Mohammadali & Niknam, Taher & Hashemi, Sattar & Lehtonen, Matti, 2020. "A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration," Energy, Elsevier, vol. 205(C).
- Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
- Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
- Feng, Cong & Cui, Mingjian & Hodge, Bri-Mathias & Zhang, Jie, 2017. "A data-driven multi-model methodology with deep feature selection for short-term wind forecasting," Applied Energy, Elsevier, vol. 190(C), pages 1245-1257.
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- Dongdong Zhang & Cunhao Rong & Hui Hwang Goh & Hui Liu & Xiang Li & Hongyu Zhu & Thomas Wu, 2023. "Reform of Electrical Engineering Undergraduate Teaching and the Curriculum System in the Context of the Energy Internet," Sustainability, MDPI, vol. 15(6), pages 1-37, March.
- Bożena Gajdzik & Magdalena Jaciow & Radosław Wolniak & Robert Wolny & Wieslaw Wes Grebski, 2023. "Energy Behaviors of Prosumers in Example of Polish Households," Energies, MDPI, vol. 16(7), pages 1-26, March.
- Xiaoqing Bai & Chun Wei & Peijie Li & Dongliang Xiao, 2023. "Editorial for the Special Issue on Sustainable Power Systems and Optimization," Sustainability, MDPI, vol. 15(6), pages 1-3, March.
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Keywords
dynamic segmented curve matching; LST-Atten; power prosumer; power prediction; trend feature perception;All these keywords.
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