Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings
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- Khan, Zulfiqar Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2023. "Dual stream network with attention mechanism for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 338(C).
- Chitalia, Gopal & Pipattanasomporn, Manisa & Garg, Vishal & Rahman, Saifur, 2020. "Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks," Applied Energy, Elsevier, vol. 278(C).
- Altaf Hussain & Zulfiqar Ahmad Khan & Tanveer Hussain & Fath U Min Ullah & Seungmin Rho & Sung Wook Baik & Chun Wei, 2022. "A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting," Complexity, Hindawi, vol. 2022, pages 1-12, October.
- Luca Di Persio & Nicola Fraccarolo, 2023. "Energy Consumption Forecasts by Gradient Boosting Regression Trees," Mathematics, MDPI, vol. 11(5), pages 1-17, February.
- Hao, Yan & Tian, Chengshi, 2019. "A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting," Applied Energy, Elsevier, vol. 238(C), pages 368-383.
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Keywords
power system; data driven; load forecasting; hybrid model;All these keywords.
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