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Article

Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

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Department of Science, Technology, Engineering and Mathematics, American University of Afghanistan, Darul Aman 4001, Kabul, Afghanistan
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Institute of Power Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
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Department of Electrical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
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Department of Electrical Engineering, United Arab Emirates University, Al Ain 15551, Abu Dhabi, United Arab Emirates
*
Authors to whom correspondence should be addressed.
Academic Editor: Jesús Polo
Energies 2021, 14(4), 1213; https://doi.org/10.3390/en14041213
Received: 24 December 2020 / Revised: 21 January 2021 / Accepted: 22 January 2021 / Published: 23 February 2021
Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models. View Full-Text
Keywords: solar irradiance; short time interval; hybrid AI prediction models; short-term solar irradiance prediction; energy management solar irradiance; short time interval; hybrid AI prediction models; short-term solar irradiance prediction; energy management
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MDPI and ACS Style

Aljanad, A.; Tan, N.M.L.; Agelidis, V.G.; Shareef, H. Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm. Energies 2021, 14, 1213. https://doi.org/10.3390/en14041213

AMA Style

Aljanad A, Tan NML, Agelidis VG, Shareef H. Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm. Energies. 2021; 14(4):1213. https://doi.org/10.3390/en14041213

Chicago/Turabian Style

Aljanad, Ahmed, Nadia M.L. Tan, Vassilios G. Agelidis, and Hussain Shareef. 2021. "Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm" Energies 14, no. 4: 1213. https://doi.org/10.3390/en14041213

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