Improving Solar Radiation Forecasting in Cloudy Conditions by Integrating Satellite Observations

Qiangsheng Bu, Shuyi Zhuang, Fei Luo, Zhigang Ye (), Yubo Yuan, Tianrui Ma and Tao Da
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Qiangsheng Bu: State Grid Jiangsu Electric Power Company Ltd. Research Institute, Nanjing 211103, China
Shuyi Zhuang: State Grid Jiangsu Electric Power Company Ltd. Research Institute, Nanjing 211103, China
Fei Luo: State Grid Jiangsu Electric Power Company Ltd. Research Institute, Nanjing 211103, China
Zhigang Ye: State Grid Jiangsu Electric Power Company Ltd. Research Institute, Nanjing 211103, China
Yubo Yuan: State Grid Jiangsu Electric Power Company Ltd. Research Institute, Nanjing 211103, China
Tianrui Ma: State Grid Jiangsu Electric Power Company Ltd. Zhenjiang Power Supply Branch, Zhenjiang 212002, China
Tao Da: State Grid Jiangsu Electric Power Company Ltd. Zhenjiang Power Supply Branch, Zhenjiang 212002, China

Energies, 2024, vol. 17, issue 24, 1-20

Abstract: Solar radiation forecasting is the basis of building a robust solar power system. Most ground-based forecasting methods are unable to consider the impact of cloud changes on future solar radiation. To alleviate this limitation, this study develops a hybrid network which relies on a convolutional neural network to extract cloud motion patterns from time series of satellite observations and a long short-term memory neural network to establish the relationship between future solar radiation and cloud information, as well as antecedent measurements. We carefully select the optimal scales to consider the spatial and temporal correlations of solar radiation and design test experiments at ten stations to check the model performance in various climate zones. The results demonstrate that the solar radiation forecasting accuracy is considerably improved, particularly in cloudy conditions, compared with purely ground-based models. The maximum magnitude of improvements reaches up to 50 W/m 2 (15%) in terms of the (relative) root mean squared error (RMSE) for 1 h ahead forecasts. The network achieves superior forecasts with correlation coefficients varying from 0.96 at 1 h ahead to 0.85 at 6 h ahead. Forecast errors are related to cloud regimes, of which the cloud amount leads to a maximum relative RMSE difference of about 50% with an additional 5% from cloud variability. This study ascertains that multi-source data fusion contributes to a better simulation of cloud impacts and a combination of different deep learning techniques enables more reliable forecasts of solar radiation. In addition, multi-step forecasts with a low latency make the advance planning and management of solar energy possible in practical applications.

Keywords: solar radiation forecasting; convolutional neural network; cloud amount; temporal and spatial scale; solar energy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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