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Accurate mechanisms for forecasting solar irradiance and insolation provideimportant information for the planning of renewable energy and agricultureprojects as well as for environmental and socio-economical studies. Thisresearch introduces a pipeline for the one-day ahead forecasting of solarirradiance and insolation that only requires solar irradiance historical datafor training. Furthermore, our approach is able to deal with missing data sinceit includes a data imputation state. In the prediction stage, we consider fourdata-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 areal-world dataset collected with 12 Automatic Weather Stations (AWS) locatedin the Nari\~no - Colombia. The results show that the neural network-basedmodels outperform ARIMA in most cases. Furthermore, LSTM exhibits betterperformance in cloudy environments (where more randomness is expected).

Short-term forecasting of global solar irradiance with incomplete data