Optimization of Neuro-controller Application for Maximum Power Point Tracking Photovoltaic Systems Through Shannon’s Information Criteria

Isman Okieh, Oubah; Seker, Serhat; Akinci, Tahir Cetin; Ibrahim Idriss, Abdoulkader

doi:10.1080/15325008.2024.2328799

Electric Power Components and Systems

2024

DOI: 10.1080/15325008.2024.2328799

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Optimization of Neuro-controller Application for Maximum Power Point Tracking Photovoltaic Systems Through Shannon’s Information Criteria

Oubah Isman Okieh,

Serhat Seker,

Tahir Cetin Akinci

et al.

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2024

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Cited by 2 publications

References 30 publications

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Techno-economic-environmental optimization of hybrid photovoltaic-thermoelectric generator systems based on data-driven approach

Yang,

Xie,

Shu

et al. 2024

Applied Thermal Engineering

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Techno-economic-environmental optimization of hybrid photovoltaic-thermoelectric generator systems based on data-driven approach

Yang,

Xie,

Shu

et al. 2024

Applied Thermal Engineering

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

Isman Okieh,

Seker,

Gokce

et al. 2024

Energies

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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/m2, a root mean square error (RMSE) of 16.30 W/m2, and a Coefficient of Determination (R2) 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.

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Optimization of Neuro-controller Application for Maximum Power Point Tracking Photovoltaic Systems Through Shannon’s Information Criteria

Cited by 2 publications

References 30 publications

Techno-economic-environmental optimization of hybrid photovoltaic-thermoelectric generator systems based on data-driven approach

Techno-economic-environmental optimization of hybrid photovoltaic-thermoelectric generator systems based on data-driven approach

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

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