Machine learning for site-adaptation and solar radiation forecasting

Gabriel Narvaez, Luis Felipe Giraldo, Michael Bressan and Andres Pantoja

Renewable Energy, 2021, vol. 167, issue C, 333-342

Abstract: Optimal management for solar energy systems requires quality data to build accurate models for predicting the behavior of solar radiation. Solar irradiance and environmental data are provided by satellite and in-situ measurements. It is usual that satellite measurements present high temporal resolution with limited spatial resolution, and in-situ measurements provide high accuracy but significant missing data. This paper proposes a methodology based on machine learning algorithms that: i) takes the best of both data sources to obtain an improved spatio-temporal resolution, known as site-adaptation; and ii) provides highly accurate forecasting solar-radiation models based on deep learning on the improved data. Through a study case with real data, we show the benefits of using the proposed methodology based on machine and deep learning techniques to integrate data from different sources and to construct precise solar radiation forecasting models in regions where solar energy systems are required. Results show that machine learning models for site-adaptation performed up to 38% better than traditional methods.

Keywords: Renewable energy; Photovoltaic systems; Solar forecasting; Deep learning; Site-adaptation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (22)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:167:y:2021:i:c:p:333-342

DOI: 10.1016/j.renene.2020.11.089

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