An Enhanced Forecasting Method of Daily Solar Irradiance in Southwestern France: A Hybrid Nonlinear Autoregressive with Exogenous Inputs with Long Short-Term Memory Approach

Oubah Isman Okieh (), Serhat Seker, Seckin Gokce and Martin Dennenmoser
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Oubah Isman Okieh: Electrical Engineering, Istanbul Technical University, 34469 Istanbul, Turkey
Serhat Seker: Electrical Engineering, Istanbul Technical University, 34469 Istanbul, Turkey
Seckin Gokce: BayWa r.e. Solar Projects GmbH, 81925 München, Germany
Martin Dennenmoser: BayWa r.e. Solar Projects GmbH, 81925 München, Germany

Energies, 2024, vol. 17, issue 16, 1-21

Abstract: The increasing global reliance on renewable energy sources, particularly solar energy, underscores the critical importance of accurate solar irradiance forecasting. As solar capacity continues to grow, precise predictions of solar irradiance become essential for optimizing the performance and reliability of photovoltaic (PV) systems. This study introduces a novel hybrid forecasting model that integrates Nonlinear Autoregressive with Exogenous Inputs (NARX) with Long Short-Term Memory (LSTM) networks. The purpose is to enhance the precision of predicting daily solar irradiance in fluctuating meteorological scenarios, particularly in southwestern France. The hybrid model employs the NARX model’s capacity to handle complex non-linear relationships and the LSTM’s aptitude to manage long-term dependencies in time-series data. The performance metrics of the hybrid NARX-LSTM model were thoroughly assessed, revealing a mean absolute error (MAE) of 9.58 W/m 2 , a root mean square error (RMSE) of 16.30 W/m 2 , and a Coefficient of Determination (R 2 ) of 0.997. Consequently, the proposed hybrid model outperforms the benchmark model in all metrics, showing a significant improvement in prediction accuracy and better alignment with the observed data. These results highlight the model’s effectiveness in enhancing forecasting accuracy under unpredictable conditions, improving solar energy integration into power systems, and ensuring more reliable energy predictions.

Keywords: solar irradiance forecasting; NARX; LSTM; prediction accuracy; solar energy integration; photovoltaic systems; unpredictable conditions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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