IDEAS home Printed from https://ideas.repec.org/p/hal/journl/hal-04779953.html
   My bibliography  Save this paper

Forecasting photovoltaic production with neural networks and weather features

Author

Listed:
  • Stéphane Goutte

    (PSB - Paris School of Business - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université, SOURCE - SOUtenabilité et RésilienCE - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - IRD [Ile-de-France] - Institut de Recherche pour le Développement)

  • Klemens Klotzner
  • Hoang Viet Le

    (SOURCE - SOUtenabilité et RésilienCE - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - IRD [Ile-de-France] - Institut de Recherche pour le Développement)

  • Hans Jörg von Mettenheim

    (IPAG Business School)

Abstract

In this paper, we address the refinement of solar energy forecasting within a 2-day window by integrating weather forecast data and strategically employing entity embedding, with a specific focus on the Multilayer Perceptron (MLP) algorithm. Through the analysis of two years of hourly solar energy production data from 16 power plants in Northern Italy (2020-2021), our research underscores the substantial impact of weather variables on solar energy production. Notably, we explore the augmentation of forecasting models by incorporating entity embedding, with a particular emphasis on embedding techniques for both general weather descriptors and individual power plants. By highlighting the nuanced integration of entity embedding within the MLP algorithm, our study reveals a significant enhancement in forecasting accuracy compared to popular machine learning algorithms like XGBoost and LGBM, showcasing the potential of this approach for more precise solar energy forecasts.

Suggested Citation

  • Stéphane Goutte & Klemens Klotzner & Hoang Viet Le & Hans Jörg von Mettenheim, 2024. "Forecasting photovoltaic production with neural networks and weather features," Post-Print hal-04779953, HAL.
    • Handle: RePEc:hal:journl:hal-04779953
    DOI: 10.1016/j.eneco.2024.107884
    Note: View the original document on HAL open archive server: https://hal.science/hal-04779953v1
    as

    Download full text from publisher

    File URL: https://hal.science/hal-04779953v1/document
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.eneco.2024.107884?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wagner, Andreas & Ramentol, Enislay & Schirra, Florian & Michaeli, Hendrik, 2022. "Short- and long-term forecasting of electricity prices using embedding of calendar information in neural networks," Journal of Commodity Markets, Elsevier, vol. 28(C).
    2. Luo, Xing & Zhang, Dongxiao & Zhu, Xu, 2021. "Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge," Energy, Elsevier, vol. 225(C).
    3. Peter Reinhard Hansen & Asger Lunde & James M. Nason, 2005. "Model confidence sets for forecasting models," FRB Atlanta Working Paper 2005-07, Federal Reserve Bank of Atlanta.
    4. VanDeventer, William & Jamei, Elmira & Thirunavukkarasu, Gokul Sidarth & Seyedmahmoudian, Mehdi & Soon, Tey Kok & Horan, Ben & Mekhilef, Saad & Stojcevski, Alex, 2019. "Short-term PV power forecasting using hybrid GASVM technique," Renewable Energy, Elsevier, vol. 140(C), pages 367-379.
    5. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    6. Belaïd, Fateh & Al-Sarihi, Aisha & Al-Mestneer, Raed, 2023. "Balancing climate mitigation and energy security goals amid converging global energy crises: The role of green investments," Renewable Energy, Elsevier, vol. 205(C), pages 534-542.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    2. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    3. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    4. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
    5. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2019. "On the impact of outlier filtering on the electricity price forecasting accuracy," Applied Energy, Elsevier, vol. 236(C), pages 196-210.
    6. Li, Pin & He, Qi & Zhang, Jinsuo & Xia, Qiyuan, 2024. "Analyzing the impact of energy synergy and renewable energy generation on energy security: Empirical evidence from China's Yangtze River Delta region," Energy, Elsevier, vol. 302(C).
    7. Nicholas Bamegne Nambie & Philomena Dadzie & Dorcas Oye Haywood-Dadzie, 2023. "Measuring the Effect of Income Inequality, Financial Inclusion, Investment, and Unemployment, on Economic Growth in Africa: A Moderating Role of Digital Financial Technology," International Journal of Economics and Financial Issues, Econjournals, vol. 13(4), pages 111-124, July.
    8. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
    9. Naeem, Muhammad Abubakr & Arfaoui, Nadia, 2023. "Exploring downside risk dependence across energy markets: Electricity, conventional energy, carbon, and clean energy during episodes of market crises," Energy Economics, Elsevier, vol. 127(PB).
    10. Feng, Jiayu & Gao, Jintong & Hu, Bin & Wang, Ruzhu & Xu, Zhenyuan, 2024. "A mass-coupled hybrid absorption-compression heat pump with output temperature of 200 °C," Energy, Elsevier, vol. 312(C).
    11. Tao, Kejun & Zhao, Jinghao & Tao, Ye & Qi, Qingqing & Tian, Yajun, 2024. "Operational day-ahead photovoltaic power forecasting based on transformer variant," Applied Energy, Elsevier, vol. 373(C).
    12. Han Yan & Md. Qamruzzaman & Sylvia Kor, 2023. "Nexus between Green Investment, Fiscal Policy, Environmental Tax, Energy Price, Natural Resources, and Clean Energy—A Step towards Sustainable Development by Fostering Clean Energy Inclusion," Sustainability, MDPI, vol. 15(18), pages 1-25, September.
    13. Dong, Kangyin & Yang, Senmiao & Wang, Jianda & Nepal, Rabindra & Jamasb, Tooraj, 2024. "Does Geopolitical Risk Accelerate Climate Vulnerability? New Evidence from the European Green Deal," Working Papers 15-2024, Copenhagen Business School, Department of Economics.
    14. Diaa Salman & Mehmet Kusaf, 2021. "Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting," Sustainability, MDPI, vol. 13(24), pages 1-22, December.
    15. Kong, Xiangfei & Du, Xinyu & Xue, Guixiang & Xu, Zhijie, 2023. "Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism," Energy, Elsevier, vol. 282(C).
    16. Wen, Jun & Zhang, Sen & Chang, Chun-Ping & Anugrah, Donni Fajar & Affandi, Yoga, 2023. "Does climate vulnerability promote green investment under energy supply restriction?," Energy Economics, Elsevier, vol. 124(C).
    17. Massimiliano Caporin & Michael McAleer, 2010. "Ranking Multivariate GARCH Models by Problem Dimension," CARF F-Series CARF-F-219, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    18. Finhold, E. & Gärtner, C. & Grindel, R. & Heller, T. & Leithäuser, N. & Röger, E. & Schirra, F., 2023. "Optimizing the marketing of flexibility for a virtual battery in day-ahead and balancing markets: A rolling horizon case study," Applied Energy, Elsevier, vol. 352(C).
    19. Honglin Xue & Junwei Ma & Jianliang Zhang & Penghui Jin & Jian Wu & Feng Du, 2024. "Power Forecasting for Photovoltaic Microgrid Based on MultiScale CNN-LSTM Network Models," Energies, MDPI, vol. 17(16), pages 1-13, August.
    20. Li, Guanglei & Wang, Guohao & Luo, Tengqi & Hu, Yuxiao & Wu, Shouyuan & Gong, Guanghui & Song, Chenchen & Guo, Zhiling & Liu, Zhengguang, 2024. "SolarSAM: Building-scale photovoltaic potential assessment based on Segment Anything Model (SAM) and remote sensing for emerging city," Renewable Energy, Elsevier, vol. 237(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:hal-04779953. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.