Estimation of Day-Time Seeing Changes at Huairou Solar Observing Station Based on Neural Networks from 1989 to 2010 by Xing Hu, Shangbin Yang, Tengfei Song, Xingming Bao, Wenjun Sun, Yuanyong Deng, Yu Liu, Mingyu Zhao

“…Currently, daytime seeing measurements are primarily conducted using the Solar Differential Image Motion Monitor (SDIMM) or the spectral ratio method. …”
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Short time solar power forecasting using P-ELM approach by Shuqi Shi, Boyang Liu, Long Ren, Yu Liu

“…This paper proposes an accurate short-term solar power forecasting method using a hybrid machine learning algorithm, with the system trained using the pre-trained extreme learning machine (P-ELM) algorithm. …”
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Embedded solar adaptive optics telescope: achieving compact integration for high-efficiency solar observations by Naiting Gu, Hao Chen, Ao Tang, Xinlong Fan, Carlos Quintero Noda, Yawei Xiao, Libo Zhong, Xiaosong Wu, Zhenyu Zhang, Yanrong Yang, Zao Yi, Xiaohu Wu, Linhai Huang, Changhui Rao

“…To address these limitations, we propose an embedded solar adaptive optics telescope (ESAOT) that intrinsically incorporates the solar AO (SAO) subsystem within the telescope's optical train, featuring a co-designed correction chain with a single Hartmann-Shack full-wavefront sensor (HS f-WFS) and a deformable secondary mirror (DSM). …”
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Forecasting the Remaining Duration of an Ongoing Solar Flare by Jeffrey W. Reep, Will T. Barnes

“…Abstract The solar X‐ray irradiance is significantly heightened during the course of a solar flare, which can cause radio blackouts due to ionization of the atoms in the ionosphere. …”
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Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary, Muhammad Salman Saeed

“…Based on the outcomes of this study, it is concluded that the Facebook Prophet model outperforms all of the other utilized conventional machine learning models, with MAE, RMSE, and R² values of 9.876, 18.762, and 0.991 for the cloudy conditions and 11.613, 19.951 and 0.988 for the clean weather conditions, respectively. The mentioned error values are the lowest among all of the studied models, which makes Facebook Prophet the most accurate solar irradiance forecasting model for Muscat, Oman.…”
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A Model for Hourly Solar Radiation Data Generation from Daily Solar Radiation Data Using a Generalized Regression Artificial Neural Network by Tamer Khatib, Wilfried Elmenreich

“…The output layer has one node which is mean hourly solar radiation. The training and development of the proposed model are done using MATLAB and 43800 records of hourly global solar radiation. …”
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Solar FaultNet: Advanced Fault Detection and Classification in Solar PV Systems Using SwinProba‐GeNet and BaBa Optimizer Models by Praveen Kumar Balachandran, Muhammad Ammirrul Atiqi Mohd Zainuri, Faisal Alsaif

“…Experimental results show that Solar FaultNet outperforms the existing state‐of‐the‐art machine‐learning algorithms and deep‐learning architectures, achieving a precision of 99.1%, recall of 99%, F1‐score of 98.9%, and an error rate as low as 0.018%. …”
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Improving prediction of solar radiation using Cheetah Optimizer and Random Forest. by Ibrahim Al-Shourbaji, Pramod H Kachare, Abdoh Jabbari, Raimund Kirner, Digambar Puri, Mostafa Mehanawi, Abdalla Alameen

“…Quantitative analysis demonstrates that the CO-RF model surpasses other techniques, Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Network, and standalone Random Forest (RF), both in the training and testing phases of SR prediction. The proposed CO-RF model outperforms others, achieving a low MAE of 0.0365, MSE of 0.0074, and an R2 of 0.9251 on the first dataset, and an MAE of 0.0469, MSE of 0.0032, and R2 of 0.9868 on the second dataset, demonstrating significant error reduction.…”
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An Evidential Solar Irradiance Forecasting Method Using Multiple Sources of Information by Mohamed Mroueh, Moustapha Doumiati, Clovis Francis, Mohamed Machmoum

“…This performance surpasses the best reported results of 30.21 W/m² from recent comparable studies for one-day-ahead solar irradiance forecasting. Comparisons with deep learning-based methods, such as long short-term memory (LSTM) networks and recurrent neural networks (RNNs), demonstrate that the proposed approach is competitive with state-of-the-art techniques, delivering reliable predictions with significantly less training data. …”
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Estimation of solar radiation and photovoltaic power potential of Türkiye using ANFIS by Murat Ispir, Muharrem Hilmi Aksoy, Mete Kalyoncu

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Neural network for prediction solar radiation in Relizane region (Algeria) - Analysis study by Dahmani Abdennasser, Yamina Ammi, Salah Hanini

“…The best results were obtained with the structure 10-25-1 (10 inputs, 25 hidden, and 1 output neurons) presented an excellent agreement between the calculated and the experimental data during the test stage with a correlation coefficient of R = 0.9879, root means squared error of RMSE = 47.7192 (Wh/m2), mean absolute error MAE = 27.7397 (Wh/m2), and mean squared error MSE = 2.2771e+03(Wh/m2), considering a three-layer Feedforward neural network with Regularization Bayesienne (trainbr)  training algorithm, a hyperbolic tangent sigmoid and linear transfer function at the hidden and the output layer, respectively. …”
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Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data by H. T. Rüdisser, A. Windisch, U. V. Amerstorfer, C. Möstl, T. Amerstorfer, R. L. Bailey, M. A. Reiss

“…The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions.…”
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Vegetable Commodity Organ Quality Formation Simulation Model (VQSM) in Solar Greenhouses by Chen Cheng, Liping Feng, Chaoyang Dong, Xianguan Chen, Feiyun Yang, Lu Wu, Jing Yang, Chengsen Zhao, Guoyin Yuan, Zhenfa Li

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Neural network quantification for solar radiation prediction: An approach for low power devices by Brenda Alejandra Villamizar-Medina, Angelo Joseph Soto Vergel, Byron Medina-Delgado, Darwin Orlando Cardozo-Sarmiento, Dinael Guevara-Ibarra, Oriana Alexandra Lopez-Bustamante

“…Experimental design allows detailed performance evaluation of quantized neural networks, demonstrating that the TensorFlow Lite Quantized Aware model is suitable for solar radiation prediction. Metrics such as root mean square error (RMSE) of 44.24 and R² of 0.96 indicate that the selected quantized model differs from the original non-quantized model by less than 0.5% in RMSE and 0.04% in R². …”
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Application of explainable machine learning for estimating direct and diffuse components of solar irradiance by Rial A. Rajagukguk, Hyunjin Lee

“…The machine learning model called CatBoost outperforms all the solar decomposition models at every station. It achieves the lowest root mean squared error (RMSE) of 8.73% when calculating DNI. …”
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Advancing sustainable renewable energy: XGBoost algorithm for the prediction of water yield in hemispherical solar stills by Salwa Ahmad Sarow, Hasan Abbas Flayyih, Maryam Bazerkan, Luttfi A. Al-Haddad, Zainab T. Al-Sharify, Ahmed Ali Farhan Ogaili

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Explainable AI and optimized solar power generation forecasting model based on environmental conditions. by Rizk M Rizk-Allah, Lobna M Abouelmagd, Ashraf Darwish, Vaclav Snasel, Aboul Ella Hassanien

“…The XAI-based Local Interpretable and Model-independent Explanation (LIME) is adapted to identify the critical factors that influence the accuracy of the power generation forecasts model in smart solar systems. The effectiveness of the proposed X-LSTM-EO model is evaluated through the use of five metrics; R-squared (R2), root mean square error (RMSE), coefficient of variation (COV), mean absolute error (MAE), and efficiency coefficient (EC). …”
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Data-driven deep learning model for predicting ambient temperature: environment and solar energy by Reza Hassanian, Nashmin Yeganeh, Ásdís Helgadóttir, Runar Unnthorsson, Morris Riedel

“…Accurate ambient temperature forecasting enhances the precision of solar power production predictions and aids in managing power generation in hybrid wind-solar power plants.…”
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AI-Based Solar Panel Detection and Monitoring Using High-Resolution Drone Imagery by Raheleh Parsaeifar, Mojtaba Valinejadshoubi, Anthony Le Guen, Fernando Valdivieso

“…Traditional solar panel installation monitoring methods are often resource-intensive and error-prone, requiring a more effective approach. …”
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Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting by Ubaid Ahmed, Anzar Mahmood, Ahsan Raza Khan, Levin Kuhlmann, Khurram Saleem Alimgeer, Sohail Razzaq, Imran Aziz, Amin Hammad

“…Results demonstrate that when trained with the selected features, the mean absolute percentage error (MAPE) of PBNN is improved by $$46.9\%$$ , and $$73.9\%$$ for Islamabad and San Diego city datasets, respectively. …”
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