A state of art review on estimation of solar radiation with various models

Gürel, Ali Etem; Ağbulut, Ümit; Bakır, Hüseyin; Ergün, Alper; Yıldız, Gökhan

doi:10.1016/j.heliyon.2023.e13167

Cited by 28 publications

(13 citation statements)

References 114 publications

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“…For the seasonal scale, except for the forecast results at Nanjing station in May 2021, the NCP_CF system shows stable forecast performance and seasonal biases at different stations and in different seasons. Its salient adaptability thus is the largest advantage compared with other SR nowcasting methods summarized in the previous review 21 . Overall, the CF nowcasting results of the NCP_CF system have good stability, strong generalizability and non-sensitivity to geographical locations and climatic characteristics .…”

Section: Discussionmentioning

confidence: 99%

Accurate nowcasting of cloud cover at solar photovoltaic plants

Min

Pan

Zhang

et al. 2023

Preprint

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Accurate nowcasting of cloud cover or fraction (CF) and its movement is still an intractable challenge for stable solar photovoltaic (PV) electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a novel nowcasting algorithm for predicting CF at the leading time of 0–4 hours at PV plants. Based on this algorithm, a cyclically updated prediction system is also established and tested at two PV plants and several stations with CF observations in China. The results demonstrate that the CF nowcasting is efficient, high quality and adaptable. Particularly, it shows an excellent forecast performance within the first 2-hour leading time, with an average correlation coefficient close to 0.90(0.80) between predicted CF and actual CF(power generation at PV plants). Our findings highlight the benefits and potential of this technique to improve the competitiveness of solar PV energy in electricity market.

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Section: Discussionmentioning

confidence: 99%

Accurate nowcasting of cloud cover at solar photovoltaic plants

Min

Pan

Zhang

et al. 2023

Preprint

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“…For the seasonal scale, except for the forecast results at Nanjing station in May 2021, the NCP_CF system shows stable forecast performance and seasonal biases at different stations and in different seasons. Its salient adaptability thus is the largest advantage compared with other solar radiation nowcasting methods summarized in the previous review 21 . Overall, the CF nowcasting results of the NCP_CF system have good stability, strong generalizability and non-sensitivity to geographical locations and climatic characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, it is still a great challenge to predict cloud motion, formation, deformation and dissipation under complex atmospheric dynamics, geography, and climatic conditions 9 , 22 , 23 . Thus, there is still no solar radiation forecast model that can work well in every region and at every time 21 .…”

Section: Introductionmentioning

confidence: 99%

Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images

Xia,

Zhang,

Min

et al. 2024

Nat Commun

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Accurate nowcasting for cloud fraction is still intractable challenge for stable solar photovoltaic electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a nowcasting algorithm for predicting cloud fraction at the leading time of 0–4 h at photovoltaic plants. Based on this algorithm, a cyclically updated prediction system is also established and tested at five photovoltaic plants and several stations with cloud fraction observations in China. The results demonstrate that the cloud fraction nowcasting is efficient, high quality and adaptable. Particularly, it shows an excellent forecast performance within the first 2-hour leading time, with an average correlation coefficient close to 0.8 between the predicted clear sky ratio and actual power generation at photovoltaic plants. Our findings highlight the benefits and potential of this technique to improve the competitiveness of solar photovoltaic energy in electricity market.

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“…Furthermore, AI can predict and forecast energy demand, allowing for better solar energy integration into the power grid. 111,112 Nwokolo et al 113 employed six different techniques for performance prediction of several solar PV systems in Australia, using a radial basis function (RBF), MLP-ANN, switched and controlled auto regression integrated moving averages (SARIMA and CARIMA), and boosting and bagging ensemble ML models. The suggested hybrid model, which used only the quantifiable parameter of solar radiation, is most comparable to all systems' recorded PV energy output, with a comprehensive R 2 of 0.9998% and an RMSE of 0.0063 kWh.…”

Section: Applications Of Ai In Renewablementioning

confidence: 99%

“…A study conducted based on AI can also aid in detecting maintenance issues such as faulty panels or connections, thus allowing for quick repairs and preventing energy loss. Furthermore, AI can predict and forecast energy demand, allowing for better solar energy integration into the power grid. , Nwokolo et al . employed six different techniques for performance prediction of several solar PV systems in Australia, using a radial basis function (RBF), MLP-ANN, switched and controlled auto regression integrated moving averages (SARIMA and CARIMA), and boosting and bagging ensemble ML models.…”

Section: Introduction To Ai and Its Applications In Renewable Energymentioning

confidence: 99%

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

Nguyen,

Tarełko,

Sharma

et al. 2024

Energy Fuels

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Modern machine learning (ML) techniques are making inroads in every aspect of renewable energy for optimization and model prediction. The effective utilization of ML techniques for the development and scaling up of renewable energy systems needs a high degree of accountability. However, most of the ML approaches currently in use are termed black box since their work is difficult to comprehend. Explainable artificial intelligence (XAI) is an attractive option to solve the issue of poor interoperability in black-box methods. This review investigates the relationship between renewable energy (RE) and XAI. It emphasizes the potential advantages of XAI in improving the performance and efficacy of RE systems. It is realized that although the integration of XAI with RE has enormous potential to alter how energy is produced and consumed, possible hazards and barriers remain to be overcome, particularly concerning transparency, accountability, and fairness. Thus, extensive research is required to address the societal and ethical implications of using XAI in RE and to create standardized data sets and evaluation metrics. In summary, this paper shows the potential, perspectives, opportunities, and challenges of XAI application to RE system management and operation aiming to target the efficient energy-use goals for a more sustainable and trustworthy future.

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A state of art review on estimation of solar radiation with various models

Cited by 28 publications

References 114 publications

Accurate nowcasting of cloud cover at solar photovoltaic plants

Accurate nowcasting of cloud cover at solar photovoltaic plants

Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images

Potential of Explainable Artificial Intelligence in Advancing Renewable Energy: Challenges and Prospects

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