Short-term forecasting of global solar irradiance with incomplete data

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 ir...

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Bibliographic Details
Published inarXiv.org
Main Authors Hoyos-Gómez, Laura S, Ruiz-Muñoz, Jose F, Ruiz-Mendoza, Belizza J
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.06.2021
Subjects
Automatic weather stations
Autoregressive models
Forecasting
Insolation
Irradiance
Missing data
Neural networks
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Summary: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 Nariño - 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).
ISSN:2331-8422