Deep learning-based forecasting of aggregated CSP production
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DOI: 10.1016/j.matcom.2020.02.007
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- Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(8), pages 1352-1362, August.
- Prasad, Abhnil A. & Taylor, Robert A. & Kay, Merlinde, 2017. "Assessment of solar and wind resource synergy in Australia," Applied Energy, Elsevier, vol. 190(C), pages 354-367.
- P. Baldi & P. Sadowski & D. Whiteson, 2014. "Searching for exotic particles in high-energy physics with deep learning," Nature Communications, Nature, vol. 5(1), pages 1-9, September.
- Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
- Hussain, C.M. Iftekhar & Norton, Brian & Duffy, Aidan, 2017. "Technological assessment of different solar-biomass systems for hybrid power generation in Europe," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P2), pages 1115-1129.
- Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
- Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 524-524, March.
- Francis M. Lopes & Ricardo Conceição & Hugo G. Silva & Thomas Fasquelle & Rui Salgado & Paulo Canhoto & Manuel Collares-Pereira, 2019. "Short-Term Forecasts of DNI from an Integrated Forecasting System (ECMWF) for Optimized Operational Strategies of a Central Receiver System," Energies, MDPI, vol. 12(7), pages 1-18, April.
- Dominguez, R. & Baringo, L. & Conejo, A.J., 2012. "Optimal offering strategy for a concentrating solar power plant," Applied Energy, Elsevier, vol. 98(C), pages 316-325.
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Cited by:
- Salam, Abdulwahed & El Hibaoui, Abdelaaziz, 2021. "Energy consumption prediction model with deep inception residual network inspiration and LSTM," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 97-109.
- Ouyang, Tiancheng & Pan, Mingming & Huang, Youbin & Tan, Xianlin & Qin, Peijia, 2023. "Thermodynamic design and power prediction of a solar power tower integrated system using neural networks," Energy, Elsevier, vol. 278(PA).
- Xie, Qiyue & Ma, Lin & Liu, Yao & Fu, Qiang & Shen, Zhongli & Wang, Xiaoli, 2023. "An improved SSA-BiLSTM-based short-term irradiance prediction model via sky images feature extraction," Renewable Energy, Elsevier, vol. 219(P2).
- Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
- Arias, I. & Cardemil, J. & Zarza, E. & Valenzuela, L. & Escobar, R., 2022. "Latest developments, assessments and research trends for next generation of concentrated solar power plants using liquid heat transfer fluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
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Keywords
Concentrated solar power; Deep learning; Neural networks; Forecasting;All these keywords.
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