Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning

Jun Qin, Hou Jiang, Ning Lu, Ling Yao and Chenghu Zhou

Renewable and Sustainable Energy Reviews, 2022, vol. 167, issue C

Abstract: Accurate output forecasts are essential for photovoltaic projects to achieve stable power supply. Traditional forecasts based on ground observation time series are widely troubled by the phase lag issue due to the incomplete consideration of the impacts of cloud motion. With the consensus that this issue can be addressed by introducing satellite-derived cloud information, we propose an innovative framework that integrates ground and satellite observations through deep learning to enhance PV output forecasts. Cloud motion patterns are captured from satellite observations using convolutional neural networks, and the long-range spatio-temporal cloud impacts on subsequent PV outputs are established by long short-term memory network. The forecast accuracy of real-time PV output is significantly improved, with a minimum (maximum) relative root mean square error of 16% (29%). The ratio of phase lag is reduced to 15% on average. This work provides a potential for alleviating the power intermittency of solar PV system and making advance planning in solar energy utilization.

Keywords: PV output Forecast; Deep learning; Cloud impact; Phase lag; Convolutional neural network; Long short-term memory; Solar energy (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (16)

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DOI: 10.1016/j.rser.2022.112680

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