Authors:
Fereshteh Jafari
1
;
2
;
Joseph Moerschell
1
and
Kaspar Riesen
2
Affiliations:
1
Institute of System Engineering, University of Applied Sciences and Arts Western Switzerland, Sion, Switzerland
;
2
Institute of Computer Science, University of Bern, CH-3012 Bern, Switzerland
Keyword(s):
LSTM, Meteorological Data Integration, Photovoltaic Power Prediction, Prediction Horizon Assessment, Temporal Sliding Window Analysis.
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 predic
tion 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.
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