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Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data

Author

Listed:
  • Javier López Gómez

    (GTE Research Group, School of Industrial Engineering, University of Vigo, Campus Lagoas-Marcosende, 36310 Vigo, Spain)

  • Ana Ogando Martínez

    (GTE Research Group, School of Industrial Engineering, University of Vigo, Campus Lagoas-Marcosende, 36310 Vigo, Spain)

  • Francisco Troncoso Pastoriza

    (GTE Research Group, School of Industrial Engineering, University of Vigo, Campus Lagoas-Marcosende, 36310 Vigo, Spain)

  • Lara Febrero Garrido

    (Defence University Centre, Spanish Naval Academy, 36920 Marín, Spain)

  • Enrique Granada Álvarez

    (GTE Research Group, School of Industrial Engineering, University of Vigo, Campus Lagoas-Marcosende, 36310 Vigo, Spain)

  • José Antonio Orosa García

    (Department of Nautical Science and Marine Engineering, Universidade da Coruña, 15011 A Coruña, Spain)

Abstract

The monitoring of power generation installations is key for modelling and predicting their future behaviour. Many renewable energy generation systems, such as photovoltaic panels and wind turbines, strongly depend on weather conditions. However, in situ measurements of relevant weather variables are not always taken into account when designing monitoring systems, and only power output is available. This paper aims to combine data from a Numerical Weather Prediction model with machine learning tools in order to accurately predict the power generation from a photovoltaic system. An Artificial Neural Network (ANN) model is used to predict power outputs from a real installation located in Puglia (southern Italy) using temperature and solar irradiation data taken from the Global Data Assimilation System (GDAS) sflux model outputs. Power outputs and weather monitoring data from the PV installation are used as a reference dataset. Three training and testing scenarios are designed. In the first one, weather data monitoring is used to both train the ANN model and predict power outputs. In the second one, training is done with monitoring data, but GDAS data is used to predict the results. In the last set, both training and result prediction are done by feeding GDAS weather data into the ANN model. The results show that the tested numerical weather model can be combined with machine learning tools to model the output of PV systems with less than 10% error, even when in situ weather measurements are not available.

Suggested Citation

  • Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:24:p:10295-:d:459487
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    References listed on IDEAS

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    4. Elias Roumpakias & Tassos Stamatelos, 2023. "Comparative Performance Analysis of a Grid-Connected Photovoltaic Plant in Central Greece after Several Years of Operation Using Neural Networks," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
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    7. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    8. Hsin-Wei Chiu & Le-Ren Chang-Chien & Chin-Chung Wu, 2021. "Construction of a Frequency Compliant Unit Commitment Framework Using an Ensemble Learning Technique," Energies, MDPI, vol. 14(2), pages 1-19, January.
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