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Open AccessArticle

Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning

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Electrical Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia
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King Abdulaziz City for Science and Technology (KACST)-Technology Innovation Center (TIC) in Radio Frequency and Photonics (RFTONICS), King Saud University, Riyadh 11421, Saudi Arabia
*
Author to whom correspondence should be addressed.
Electronics 2020, 9(10), 1700; https://doi.org/10.3390/electronics9101700
Received: 11 September 2020 / Revised: 30 September 2020 / Accepted: 12 October 2020 / Published: 16 October 2020
(This article belongs to the Section Computer Science & Engineering)
Ahead-of-time forecasting of the output power of power plants is essential for the stability of the electricity grid and ensuring uninterrupted service. However, forecasting renewable energy sources is difficult due to the chaotic behavior of natural energy sources. This paper presents a new approach to estimate short-term solar irradiance from sky images. The proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance. The performance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images. The datasets contain over 350,000 images for an interval of 16 years, from 2004 to 2020, with the corresponding global horizontal irradiance (GHI) of each image as the ground truth. Compared to the state-of-the-art computationally heavy algorithms proposed in the literature, our approach achieves competitive results with much less computational complexity for both nowcasting and forecasting up to 4 h ahead of time. View Full-Text
Keywords: global horizontal irradiance (GHI); photovoltaics (PV); solar energy; solar irradiance forecasting global horizontal irradiance (GHI); photovoltaics (PV); solar energy; solar irradiance forecasting
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Al-lahham, A.; Theeb, O.; Elalem, K.; A. Alshawi, T.; A. Alshebeili, S. Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning. Electronics 2020, 9, 1700.

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