Prediction of Solar Radiation Using Neural Networks Forecasting

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Abstract

Solar radiation and wind data play an important role in renewable energy projects to produce electricity. In Ecuador, these data are not always available for locations of interest due to absences of meteorological stations. In the scope of this paper, a low-cost automatic meteorological station prototype based on Raspberry technology was developed to measure the aforementioned variables. The objective of this paper is twofold: a) to present a proposal for the design of a low-cost automatic weather station using the Raspberry Pi microcomputer, showing the feasibility of this technology as an alternative for the construction of automatic meteorological station and; b) to use Forecasting with neural networks to predict solar radiation in Manta, Ecuador, based on the historical data collected: solar radiation, wind speed and wind direction. We proved that both technology feasibility and Machine learning has a high potential as a tool to use in this field of study.

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Marcos, P. J., Talavera, A., Velásquez, C., & Peralta, D. T. (2021). Prediction of Solar Radiation Using Neural Networks Forecasting. In Communications in Computer and Information Science (Vol. 1410 CCIS, pp. 181–194). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-76228-5_13

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