  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).
