# Sequence to sequence deep learning models for solar irradiation forecasting

## TL;DR

This paper explores deep learning sequence-to-sequence models, especially LSTM, for short-term solar irradiation forecasting, demonstrating improved accuracy over traditional machine learning methods by incorporating spatio-temporal features.

## Contribution

It introduces deep learning models for GHI prediction, highlighting the effectiveness of LSTM and the importance of spatio-temporal features for improved accuracy.

## Key findings

- LSTM outperforms other models in GHI forecasting.
- Spatio-temporal features significantly enhance prediction accuracy.
- Deep learning models outperform traditional techniques like GBRT and FFNN.

## Abstract

The energy output a photo voltaic(PV) panel is a function of solar irradiation and weather parameters like temperature and wind speed etc. A general measure for solar irradiation called Global Horizontal Irradiance (GHI), customarily reported in Watt/meter$^2$, is a generic indicator for this intermittent energy resource. An accurate prediction of GHI is necessary for reliable grid integration of the renewable as well as for power market trading. While some machine learning techniques are well introduced along with the traditional time-series forecasting techniques, deep-learning techniques remains less explored for the task at hand. In this paper we give deep learning models suitable for sequence to sequence prediction of GHI. The deep learning models are reported for short-term forecasting $\{1-24\}$ hour along with the state-of-the art techniques like Gradient Boosted Regression Trees(GBRT) and Feed Forward Neural Networks(FFNN). We have checked that spatio-temporal features like wind direction, wind speed and GHI of neighboring location improves the prediction accuracy of the deep learning models significantly. Among the various sequence-to-sequence encoder-decoder models LSTM performed superior, handling short-comings of the state-of-the-art techniques.