Short-term forecasting of global solar irradiance with incomplete data

• URL: http://arxiv.org/abs/2106.06868v1
• Date: Sat, 12 Jun 2021 21:44:43 GMT
• Title: Short-term forecasting of global solar irradiance with incomplete data
• Authors: Laura S. Hoyos-G\'omez, Jose F. Ruiz-Mu\~noz, Belizza J. Ruiz-Mendoza
• Abstract summary: This research introduces a pipeline for the one-day ahead forecasting of solar irradiance and insolation. We consider four data-driven approaches: Autoregressive Integrated Moving Average (ARIMA), Single Layer Feed Forward Network (SL-FNN), Multiple Layer Feed Forward Network (FL-FNN) and Long Short-Term Memory (LSTM) The experiments are performed in a real-world dataset collected with 12 Automatic Weather Stations (AWS) located in the Narino - Colombia.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate mechanisms for forecasting solar irradiance and insolation provide important information for the planning of renewable energy and agriculture projects as well as for environmental and socio-economical studies. This research introduces a pipeline for the one-day ahead forecasting of solar irradiance and insolation that only requires solar irradiance historical data for training. Furthermore, our approach is able to deal with missing data since it includes a data imputation state. In the prediction stage, we consider four data-driven approaches: Autoregressive Integrated Moving Average (ARIMA), Single Layer Feed Forward Network (SL-FNN), Multiple Layer Feed Forward Network (FL-FNN), and Long Short-Term Memory (LSTM). The experiments are performed in a real-world dataset collected with 12 Automatic Weather Stations (AWS) located in the Nari\~no - Colombia. The results show that the neural network-based models outperform ARIMA in most cases. Furthermore, LSTM exhibits better performance in cloudy environments (where more randomness is expected).

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