Ensemble solar forecasting and post-processing using dropout neural network and information from neighboring satellite pixels

Gokhan Mert Yagli, Dazhi Yang and Dipti Srinivasan

Renewable and Sustainable Energy Reviews, 2022, vol. 155, issue C

Abstract: Ensemble weather forecasts are often found to be under-dispersed and biased. Post-processing using spatio-temporal information is, therefore, required if one wishes to improve the quality of the raw forecasts. It is on this account that the present article generates and post-processes ensemble solar forecasts using satellite-derived irradiance not only from the focal pixel but also from the neighboring pixels. The ensemble forecasting model of choice is a dropout neural network with Monte Carlo sampling, eliminating the need for training multiple models and ensuring parameter diversity in ensemble forecasting. Subsequently, ensemble forecasts are post-processed using both parametric and nonparametric post-processing techniques, such as nonhomogenous regression, generalized additive model, linear quantile regression, or quantile random forests. The proposed forecasting framework is demonstrated and verified using four years of half-hourly data, at seven locations in the United States. Continuous ranked probability skill scores as high as 66% have been obtained when comparing the proposed method to a conditional climatology reference. The content of this article may be useful to a wide range of stakeholders in the power system, including but not limited to: independent system operators, who aim at efficiently maintaining the system’s reliability; utility- and distributed-scale PV plant owners, who wish to avoid penalties for power deviation between the scheduled and real-time delivery; and forecast retailers, who can benefit from selling solar forecasts of higher quality.

Keywords: Dropout neural network; Ensemble solar forecasting; Machine learning; Monte Carlo sampling; Post-processing; Satellite-derived irradiance (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.rser.2021.111909

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