Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods
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DOI: 10.1016/j.apenergy.2021.117540
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Citations
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- Carlo Andrea Bollino & Maria Chiara D’Errico, 2022. "Electricity Demand Elasticity, Mobility, and COVID-19 Contagion Nexus in the Italian Day-Ahead Electricity Market," Energies, MDPI, vol. 15(20), pages 1-26, October.
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- Ku, Arthur Lin & Qiu, Yueming (Lucy) & Lou, Jiehong & Nock, Destenie & Xing, Bo, 2022. "Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona," Applied Energy, Elsevier, vol. 310(C).
- Zhang, Yunxin & Guo, Huan & Sun, Ming & Liu, Sifeng & Forrest, Jeffrey, 2023. "A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting," Energy, Elsevier, vol. 264(C).
- Gao, Tian & Niu, Dongxiao & Ji, Zhengsen & Sun, Lijie, 2022. "Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm," Energy, Elsevier, vol. 261(PB).
- Zhou, Wenhao & Li, Hailin & Zhang, Zhiwei, 2022. "A novel seasonal fractional grey model for predicting electricity demand: A case study of Zhejiang in China," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 128-147.
- Wang, Jianzhou & Xing, Qianyi & Zeng, Bo & Zhao, Weigang, 2022. "An ensemble forecasting system for short-term power load based on multi-objective optimizer and fuzzy granulation," Applied Energy, Elsevier, vol. 327(C).
- Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2023. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms," Energies, MDPI, vol. 16(11), pages 1-23, June.
- Shen, Yun & Mao, Yaqian & Weng, Jiacheng & Wu, Chenxi & Wu, Haixin & Gu, Yangyang & Wang, Jianhong, 2024. "A novel SARCIMA model based on central difference and its application in solar power generation of China," Applied Energy, Elsevier, vol. 360(C).
- Weijie Zhou & Huihui Tao & Jiaxin Chang & Huimin Jiang & Li Chen, 2023. "Forecasting Chinese Electricity Consumption Based on Grey Seasonal Model with New Information Priority," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
- Mustafa Saglam & Catalina Spataru & Omer Ali Karaman, 2022. "Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island," Energies, MDPI, vol. 15(16), pages 1-22, August.
- Du, Pei & Guo, Ju'e & Sun, Shaolong & Wang, Shouyang & Wu, Jing, 2022. "A novel two-stage seasonal grey model for residential electricity consumption forecasting," Energy, Elsevier, vol. 258(C).
- Lao, Tongfei & Sun, Yanrui, 2022. "Predicting the production and consumption of natural gas in China by using a new grey forecasting method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 295-315.
- Ye, Li & Dang, Yaoguo & Fang, Liping & Wang, Junjie, 2023. "A nonlinear interactive grey multivariable model based on dynamic compensation for forecasting the economy-energy-environment system," Applied Energy, Elsevier, vol. 331(C).
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Keywords
Covid-19; Forecasting; Electricity generation; Fractional grey model; Machine learning; Seasonal fluctuations;All these keywords.
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