# Short-term forecasting of global solar irradiance with incomplete data

arXiv: https://arxiv.org/abs/2106.06868 · HTML: https://mlsanity.com/paper/2106.06868/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 62 references that are in the map.
- First posted on arXiv 12 Jun 2021.

## Builds on (most cited first, 1 of 1)

- [Global solar irradiation prediction using a multi-gene genetic programming approach](https://mlsanity.com/paper/1403.0623/) (2014)

## Abstract (arXiv)

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).