Short-term forecasting of global solar irradiance with incomplete data
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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).
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