Abstract
Electric power generated from Solar Photovoltaic (PV) panels is estimated to have increased globally by 22% in 2019, to 720 TWh [5]. It is now considered the third-largest renewable energy technology after wind and hydro powers. The primary reason for this growth is the need to utilize free energy resources that are also environmentally clean. PV-generated power, however, is uncertain and varies from time to time and season to season. Dealing with this uncertainty requires having predictive and forecasting models that accurately estimate generated power from historical data. This paper reports on an in-progress research project that explores weather-related variables such as humidity, temperature, and wind speed and uses them to predict and forecast generated power using a dataset collected over three years by a weather station at Southeast New Mexico College in Carlsbad, New Mexico. Various predictive and forecasting models are built, trained, and evaluated. The goal is to explore these variables and report on what makes a good predictive model and how such a model behaves over time.
J. Al-Nouman and A. Al-Gahmi—These authors contributed equally to this work.
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Acknowledgements
The HSI Grant Services at Southeast New Mexico College (formerly New Mexico State University - Carlsbad) provided the weather station utilized in this study. The authors would like to acknowledge this support.
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Al-Nouman, J., Al-Gahmi, A. (2022). Predicting Solar PV Generation Using Weather Station Data. In: Ghosh, A.K., Rixham, C. (eds) Proceedings of the American Solar Energy Society National Conference. ASES SOLAR 2022. Springer Proceedings in Energy. Springer, Cham. https://doi.org/10.1007/978-3-031-08786-8_18
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