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Power Forecasting of a Photovoltaic Plant Located in ENEA Casaccia Research Center

Author

Listed:
  • Martina Radicioni

    (Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy)

  • Valentina Lucaferri

    (Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy)

  • Francesco De Lia

    (Casaccia Research Center, ENEA, Via Anguillarese 301, 00060 Rome, Italy)

  • Antonino Laudani

    (Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy)

  • Roberto Lo Presti

    (Casaccia Research Center, ENEA, Via Anguillarese 301, 00060 Rome, Italy)

  • Gabriele Maria Lozito

    (Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy)

  • Francesco Riganti Fulginei

    (Department of Engineering, University of Roma Tre, Via Vito Volterra 62, 00146 Rome, Italy)

  • Riccardo Schioppo

    (Casaccia Research Center, ENEA, Via Anguillarese 301, 00060 Rome, Italy)

  • Mario Tucci

    (Casaccia Research Center, ENEA, Via Anguillarese 301, 00060 Rome, Italy)

Abstract

This work proposes an Artificial Neural Network (ANN) able to provide an accurate forecasting of power produced by photovoltaic (PV) plants. The ANN is customized on the basis of the particular season of the year. An accurate analysis of input variables, i.e., solar irradiance, temperature and air humidity, carried out by means of Pearson Correlation, has allowed to select, day by day, the most suitable set of inputs and ANN architecture also to reduce the necessity of large computational resource. Thus, features are added to the ANN as needed, avoiding waste of computational resources. The method has been validated through data collected from a PV plant installed in ENEA (National agency for new technologies, energy and sustainable economic development) Research Center, located in Casaccia, Rome (Italy). The developed strategy is able to furnish accurate predictions even in the case of strong irregularities of solar irradiance, providing accurate results in rapidly changing scenarios.

Suggested Citation

  • Martina Radicioni & Valentina Lucaferri & Francesco De Lia & Antonino Laudani & Roberto Lo Presti & Gabriele Maria Lozito & Francesco Riganti Fulginei & Riccardo Schioppo & Mario Tucci, 2021. "Power Forecasting of a Photovoltaic Plant Located in ENEA Casaccia Research Center," Energies, MDPI, vol. 14(3), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:707-:d:489843
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    References listed on IDEAS

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    Cited by:

    1. Leidy Gutiérrez & Julian Patiño & Eduardo Duque-Grisales, 2021. "A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction," Energies, MDPI, vol. 14(15), pages 1-16, July.
    2. Miguel López Santos & Xela García-Santiago & Fernando Echevarría Camarero & Gonzalo Blázquez Gil & Pablo Carrasco Ortega, 2022. "Application of Temporal Fusion Transformer for Day-Ahead PV Power Forecasting," Energies, MDPI, vol. 15(14), pages 1-22, July.
    3. Varaha Satra Bharath Kurukuru & Ahteshamul Haque & Mohammed Ali Khan & Subham Sahoo & Azra Malik & Frede Blaabjerg, 2021. "A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems," Energies, MDPI, vol. 14(15), pages 1-35, August.
    4. Akash Kumar & Bing Yan & Ace Bilton, 2022. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction," Energies, MDPI, vol. 15(18), pages 1-23, September.
    5. Fateh Mehazzem & Maina André & Rudy Calif, 2022. "Efficient Output Photovoltaic Power Prediction Based on MPPT Fuzzy Logic Technique and Solar Spatio-Temporal Forecasting Approach in a Tropical Insular Region," Energies, MDPI, vol. 15(22), pages 1-21, November.

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