Day-Ahead Forecasting of the Percentage of Renewables Based on Time-Series Statistical Methods
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- González-Aparicio, I. & Zucker, A., 2015. "Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain," Applied Energy, Elsevier, vol. 159(C), pages 334-349.
- Demirhan, Haydar & Renwick, Zoe, 2018. "Missing value imputation for short to mid-term horizontal solar irradiance data," Applied Energy, Elsevier, vol. 225(C), pages 998-1012.
- Peter Bauer & Alan Thorpe & Gilbert Brunet, 2015. "The quiet revolution of numerical weather prediction," Nature, Nature, vol. 525(7567), pages 47-55, September.
- Scheu, Matti Niclas & Kolios, Athanasios & Fischer, Tim & Brennan, Feargal, 2017. "Influence of statistical uncertainty of component reliability estimations on offshore wind farm availability," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 28-39.
- Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
- Majid Mehrasa & Edris Pouresmaeil & Bahram Pournazarian & Amir Sepehr & Mousa Marzband & João P. S. Catalão, 2018. "Synchronous Resonant Control Technique to Address Power Grid Instability Problems Due to High Renewables Penetration," Energies, MDPI, vol. 11(9), pages 1-18, September.
- Tomasz Ciechulski & Stanisław Osowski, 2020. "Deep Learning Approach to Power Demand Forecasting in Polish Power System," Energies, MDPI, vol. 13(22), pages 1-13, November.
- Tomasz Ciechulski & Stanisław Osowski, 2021. "High Precision LSTM Model for Short-Time Load Forecasting in Power Systems," Energies, MDPI, vol. 14(11), pages 1-15, May.
- Neves, Diana & Brito, Miguel C. & Silva, Carlos A., 2016. "Impact of solar and wind forecast uncertainties on demand response of isolated microgrids," Renewable Energy, Elsevier, vol. 87(P2), pages 1003-1015.
- Dengyong Zhang & Haixin Tong & Feng Li & Lingyun Xiang & Xiangling Ding, 2020. "An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model," Energies, MDPI, vol. 13(18), pages 1-14, September.
- Robert Basmadjian, 2019. "Flexibility-Based Energy and Demand Management in Data Centers: A Case Study for Cloud Computing," Energies, MDPI, vol. 12(17), pages 1-22, August.
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Keywords
time-series; auto-regression; moving average; forecasting models; percentage of renewable energy sources;All these keywords.
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