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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

    as
    1. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    2. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    3. Fabio Corti & Antonino Laudani & Gabriele Maria Lozito & Alberto Reatti, 2020. "Computationally Efficient Modeling of DC-DC Converters for PV Applications," Energies, MDPI, vol. 13(19), pages 1-18, September.
    4. Kalogirou, Soteris A., 2001. "Artificial neural networks in renewable energy systems applications: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(4), pages 373-401, December.
    5. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    6. Liu, Luyao & Zhao, Yi & Chang, Dongliang & Xie, Jiyang & Ma, Zhanyu & Sun, Qie & Yin, Hongyi & Wennersten, Ronald, 2018. "Prediction of short-term PV power output and uncertainty analysis," Applied Energy, Elsevier, vol. 228(C), pages 700-711.
    7. Dukhwan Yu & Seowoo Lee & Sangwon Lee & Wonik Choi & Ling Liu, 2020. "Forecasting Photovoltaic Power Generation Using Satellite Images," Energies, MDPI, vol. 13(24), pages 1-15, December.
    8. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
    9. Utpal Kumar Das & Kok Soon Tey & Mehdi Seyedmahmoudian & Mohd Yamani Idna Idris & Saad Mekhilef & Ben Horan & Alex Stojcevski, 2017. "SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions," Energies, MDPI, vol. 10(7), pages 1-17, June.
    10. Touati, Farid & Chowdhury, Noor Alam & Benhmed, Kamel & San Pedro Gonzales, Antonio J.R. & Al-Hitmi, Mohammed A. & Benammar, Mohieddine & Gastli, Adel & Ben-Brahim, Lazhar, 2017. "Long-term performance analysis and power prediction of PV technology in the State of Qatar," Renewable Energy, Elsevier, vol. 113(C), pages 952-965.
    11. Fabio Corti & Alberto Reatti & Andrea Nepote & Luca Pugi & Marco Pierini & Libero Paolucci & Francesco Grasso & Emanuele Grasso & Matthias Nienhause, 2020. "A Secondary-Side Controlled Electric Vehicle Wireless Charger," Energies, MDPI, vol. 13(24), pages 1-19, December.
    12. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    13. Bugała, A. & Zaborowicz, M. & Boniecki, P. & Janczak, D. & Koszela, K. & Czekała, W. & Lewicki, A., 2018. "Short-term forecast of generation of electric energy in photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 306-312.
    14. Fernandez-Jimenez, L. Alfredo & Muñoz-Jimenez, Andrés & Falces, Alberto & Mendoza-Villena, Montserrat & Garcia-Garrido, Eduardo & Lara-Santillan, Pedro M. & Zorzano-Alba, Enrique & Zorzano-Santamaria,, 2012. "Short-term power forecasting system for photovoltaic plants," Renewable Energy, Elsevier, vol. 44(C), pages 311-317.
    15. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
    16. Zhao, Pan & Wang, Jiangfeng & Xia, Junrong & Dai, Yiping & Sheng, Yingxin & Yue, Jie, 2012. "Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China," Renewable Energy, Elsevier, vol. 43(C), pages 234-241.
    17. Heydari, Azim & Astiaso Garcia, Davide & Keynia, Farshid & Bisegna, Fabio & De Santoli, Livio, 2019. "A novel composite neural network based method for wind and solar power forecasting in microgrids," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    18. Yadav, Amit Kumar & Sharma, Vikrant & Malik, Hasmat & Chandel, S.S., 2018. "Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based Radial Basis Function Neural Network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2115-2127.
    19. Vaz, A.G.R. & Elsinga, B. & van Sark, W.G.J.H.M. & Brito, M.C., 2016. "An artificial neural network to assess the impact of neighbouring photovoltaic systems in power forecasting in Utrecht, the Netherlands," Renewable Energy, Elsevier, vol. 85(C), pages 631-641.
    20. Ogliari, Emanuele & Dolara, Alberto & Manzolini, Giampaolo & Leva, Sonia, 2017. "Physical and hybrid methods comparison for the day ahead PV output power forecast," Renewable Energy, Elsevier, vol. 113(C), pages 11-21.
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    Cited by:

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    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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