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Short-Term Prediction of the Solar Photovoltaic Power Output Using Nonlinear Autoregressive Exogenous Inputs and Artificial Neural Network Techniques Under Different Weather Conditions

Author

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  • Abdulrahman Th. Mohammad

    (Baqubah Technical Institute, Middle Technical University, Baghdad 10074, Iraq)

  • Wisam A. M. Al-Shohani

    (Technical Engineering Collage, Middle Technical University, Baghdad 10074, Iraq)

Abstract

The power generation by solar photovoltaic (PV) systems will become an important and reliable source in the future. Therefore, this aspect has received great attention from researchers, who have investigated accurate and credible models to predict the power output of PV modules. This prediction is very important in the planning of short-term resources, the management of energy distribution, and the operation security for PV systems. This paper aims to explore the sensitivity of Nonlinear Autoregressive Exogenous Inputs (NARX) and an Artificial Neural Network (ANNs) as a result of weather dynamics in the very short term for predicting the power output of PV modules. This goal was achieved based on an experimental dataset for the power output of a PV module obtained during the sunny days in summer and cloudy days in winter, and using the data in the algorithm models of NARX and ANN. In addition, the analysis results of the NARX model were compared with those of the static ANN model to measure the accuracy and superiority of the nonlinear model. The results showed that the NARX model offers very good estimates and is efficient in predicting the power output of the PV module in the very short term. Thus, the coefficient of determination (R 2 ) and mean square error (MSE) were 94.4–97.9% and 0.08261–0.04613, respectively, during the summer days, and the R 2 and MSE were 90.1–89.2% and 0.281–0.249, respectively, during the winter days. Overall, it can be concluded that the sensitivity of the NARX model is more accurate in the summer days than the winter days, when the weather conditions are more stable with a gradual change. Moreover, the effectiveness of the NARX model has the specificity to learn and to generalize more effectively than the static ANN.

Suggested Citation

  • Abdulrahman Th. Mohammad & Wisam A. M. Al-Shohani, 2024. "Short-Term Prediction of the Solar Photovoltaic Power Output Using Nonlinear Autoregressive Exogenous Inputs and Artificial Neural Network Techniques Under Different Weather Conditions," Energies, MDPI, vol. 17(23), pages 1-16, December.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:23:p:6153-:d:1538157
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    References listed on IDEAS

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    1. Ferlito, S. & Adinolfi, G. & Graditi, G., 2017. "Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production," Applied Energy, Elsevier, vol. 205(C), pages 116-129.
    2. Fjelkestam Frederiksen, Cornelia A. & Cai, Zuansi, 2022. "Novel machine learning approach for solar photovoltaic energy output forecast using extra-terrestrial solar irradiance," Applied Energy, Elsevier, vol. 306(PB).
    3. Oubah Isman Okieh & Serhat Seker & Seckin Gokce & Martin Dennenmoser, 2024. "An Enhanced Forecasting Method of Daily Solar Irradiance in Southwestern France: A Hybrid Nonlinear Autoregressive with Exogenous Inputs with Long Short-Term Memory Approach," Energies, MDPI, vol. 17(16), pages 1-21, August.
    4. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    5. Claudio Monteiro & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado & Andres Muñoz-Jimenez & Pedro M. Lara-Santillan, 2013. "Short-Term Forecasting Models for Photovoltaic Plants: Analytical versus Soft-Computing Techniques," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, November.
    6. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    7. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
    8. Mohamed Ismail Vawda & Romano Lottering & Onisimo Mutanga & Kabir Peerbhay & Mbulisi Sibanda, 2024. "Comparing the Utility of Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) on Sentinel-2 MSI to Estimate Dry Season Aboveground Grass Biomass," Sustainability, MDPI, vol. 16(3), pages 1-18, January.
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