Predicting Photovoltaic Power Output Using LSTM: A Comparative Study Using both Historical and Climate Data

Fereshteh Jafari, Fereshteh Jafari, Joseph Moerschell, Kaspar Riesen

2025

Abstract

Accurate photovoltaic (PV) power output prediction is important for efficient energy management in solar power systems. This study explores the benefits and limitations of Long Short-Term Memory (LSTM) networks in predicting PV power using three distinct approaches, namely using historical PV power data, climate data, and a combination of both, all with timestamps. The performance of these methods is evaluated across different prediction horizons of 10, 30, and 50 minutes ahead. Additionally, the impact of the sliding window size, representing the amount of past data used for training, is analyzed. The models are trained and tested on a dataset collected over three months from a rooftop PV system in Sion, Switzerland, with a maximum power of 22.2 kW. The Root Mean Square Error as well as the R2 metrics are provided to assess the accuracy of each method. The results demonstrate that both the choice of the actual input data and the sliding window size significantly influence the prediction accuracy. In particular, the results presented here show the potential of combining different data sources to improve the accuracy of PV power prediction using LSTM models.

Paper Citation

in Harvard Style

Jafari F., Moerschell J. and Riesen K. (2025). Predicting Photovoltaic Power Output Using LSTM: A Comparative Study Using both Historical and Climate Data. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-730-6, SciTePress, pages 733-740. DOI: 10.5220/0013258000003905

in Bibtex Style

@conference{icpram25,
author={Fereshteh Jafari and Joseph Moerschell and Kaspar Riesen},
title={Predicting Photovoltaic Power Output Using LSTM: A Comparative Study Using both Historical and Climate Data},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2025},
pages={733-740},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013258000003905},
isbn={978-989-758-730-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Predicting Photovoltaic Power Output Using LSTM: A Comparative Study Using both Historical and Climate Data
SN - 978-989-758-730-6
AU - Jafari F.
AU - Moerschell J.
AU - Riesen K.
PY - 2025
SP - 733
EP - 740
DO - 10.5220/0013258000003905
PB - SciTePress