IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i11p3005-d370022.html
   My bibliography  Save this article

Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks

Author

Listed:
  • Gabriel Mendonça de Paiva

    (School of Electrical, Mechanical, and Computer Engineering, Federal University of Goias (UFG), Goiania 74605-010, Brazil)

  • Sergio Pires Pimentel

    (School of Electrical, Mechanical, and Computer Engineering, Federal University of Goias (UFG), Goiania 74605-010, Brazil)

  • Bernardo Pinheiro Alvarenga

    (School of Electrical, Mechanical, and Computer Engineering, Federal University of Goias (UFG), Goiania 74605-010, Brazil)

  • Enes Gonçalves Marra

    (School of Electrical, Mechanical, and Computer Engineering, Federal University of Goias (UFG), Goiania 74605-010, Brazil)

  • Marco Mussetta

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

  • Sonia Leva

    (Department of Energy, Politecnico di Milano, 20156 Milano, Italy)

Abstract

The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68% for mean absolute error (MAE) and 3.41% for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained results show that location, forecast horizon and error metric definition affect model accuracy dominance. Both Haurwitz and Ineichen clear sky models have been implemented, and the results denoted a low influence of these models in the prediction accuracy of multivariate ML forecasting. In a broad perspective, MGGP presented more accurate and robust results in single prediction cases, providing faster solutions, while ANN presented more accurate results for ensemble forecasting, although it presented higher complexity and requires additional computational effort.

Suggested Citation

  • Gabriel Mendonça de Paiva & Sergio Pires Pimentel & Bernardo Pinheiro Alvarenga & Enes Gonçalves Marra & Marco Mussetta & Sonia Leva, 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks," Energies, MDPI, vol. 13(11), pages 1-28, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:3005-:d:370022
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/11/3005/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/11/3005/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Li, Jiaming & Ward, John K. & Tong, Jingnan & Collins, Lyle & Platt, Glenn, 2016. "Machine learning for solar irradiance forecasting of photovoltaic system," Renewable Energy, Elsevier, vol. 90(C), pages 542-553.
    3. Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
    4. Nobre, André M. & Severiano, Carlos A. & Karthik, Shravan & Kubis, Marek & Zhao, Lu & Martins, Fernando R. & Pereira, Enio B. & Rüther, Ricardo & Reindl, Thomas, 2016. "PV power conversion and short-term forecasting in a tropical, densely-built environment in Singapore," Renewable Energy, Elsevier, vol. 94(C), pages 496-509.
    5. Shahriari, Mehdi & Blumsack, Seth, 2017. "Scaling of wind energy variability over space and time," Applied Energy, Elsevier, vol. 195(C), pages 572-585.
    6. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    7. Aguiar, L. Mazorra & Pereira, B. & Lauret, P. & Díaz, F. & David, M., 2016. "Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting," Renewable Energy, Elsevier, vol. 97(C), pages 599-610.
    8. Simone Sperati & Stefano Alessandrini & Pierre Pinson & George Kariniotakis, 2015. "The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation," Energies, MDPI, vol. 8(9), pages 1-26, September.
    9. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.
    10. 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.
    11. Alberto Dolara & Francesco Grimaccia & Sonia Leva & Marco Mussetta & Emanuele Ogliari, 2015. "A Physical Hybrid Artificial Neural Network for Short Term Forecasting of PV Plant Power Output," Energies, MDPI, vol. 8(2), pages 1-16, February.
    12. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Llinet Benavides Cesar & Rodrigo Amaro e Silva & Miguel Ángel Manso Callejo & Calimanut-Ionut Cira, 2022. "Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates," Energies, MDPI, vol. 15(12), pages 1-23, June.
    2. Mehmood, Faiza & Ghani, Muhammad Usman & Asim, Muhammad Nabeel & Shahzadi, Rehab & Mehmood, Aamir & Mahmood, Waqar, 2021. "MPF-Net: A computational multi-regional solar power forecasting framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    3. Renchu Guan & Aoqing Wang & Yanchun Liang & Jiasheng Fu & Xiaosong Han, 2022. "International Natural Gas Price Trends Prediction with Historical Prices and Related News," Energies, MDPI, vol. 15(10), pages 1-14, May.
    4. Hugo Gaspar Hernandez-Palma & Jonny Rafael Plaza Alvarado & Jesús Enrique García Guiliany & Guilherme Luiz Dotto & Claudete Gindri Ramos, 2024. "Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 1-10, March.
    5. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
    6. Zhong, Lingshu & Wu, Pan & Pei, Mingyang, 2024. "Wind power generation prediction during the COVID-19 epidemic based on novel hybrid deep learning techniques," Renewable Energy, Elsevier, vol. 222(C).
    7. Chan Roh, 2022. "Deep-Learning-Based Pitch Controller for Floating Offshore Wind Turbine Systems with Compensation for Delay of Hydraulic Actuators," Energies, MDPI, vol. 15(9), pages 1-18, April.
    8. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Dong-Ho Kang & Jin-Young Kang & Hae-Su Park, 2020. "Machine Learning-Based Approach to Predict Energy Consumption of Renewable and Nonrenewable Power Sources," Energies, MDPI, vol. 13(18), pages 1-16, September.
    9. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
    10. Periklis Gogas & Theophilos Papadimitriou, 2023. "Machine Learning in Renewable Energy," Energies, MDPI, vol. 16(5), pages 1-3, February.
    11. 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.
    12. 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.

    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. Samu, Remember & Calais, Martina & Shafiullah, G.M. & Moghbel, Moayed & Shoeb, Md Asaduzzaman & Nouri, Bijan & Blum, Niklas, 2021. "Applications for solar irradiance nowcasting in the control of microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    2. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    3. Nespoli, Alfredo & Niccolai, Alessandro & Ogliari, Emanuele & Perego, Giovanni & Collino, Elena & Ronzio, Dario, 2022. "Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery," Applied Energy, Elsevier, vol. 305(C).
    4. Wang, Zhenyu & Zhang, Yunpeng & Li, Guorong & Zhang, Jinlong & Zhou, Hai & Wu, Ji, 2024. "A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model," Renewable Energy, Elsevier, vol. 226(C).
    5. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model," Energies, MDPI, vol. 11(4), pages 1-15, April.
    6. Jessica Wojtkiewicz & Matin Hosseini & Raju Gottumukkala & Terrence Lynn Chambers, 2019. "Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 12(21), pages 1-13, October.
    7. Marchesoni-Acland, Franco & Alonso-Suárez, Rodrigo, 2020. "Intra-day solar irradiation forecast using RLS filters and satellite images," Renewable Energy, Elsevier, vol. 161(C), pages 1140-1154.
    8. Anh Ngoc-Lan Huynh & Ravinesh C. Deo & Duc-Anh An-Vo & Mumtaz Ali & Nawin Raj & Shahab Abdulla, 2020. "Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network," Energies, MDPI, vol. 13(14), pages 1-30, July.
    9. Zang, Haixiang & Liu, Ling & Sun, Li & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2020. "Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations," Renewable Energy, Elsevier, vol. 160(C), pages 26-41.
    10. Armando Castillejo-Cuberos & John Boland & Rodrigo Escobar, 2021. "Short-Term Deterministic Solar Irradiance Forecasting Considering a Heuristics-Based, Operational Approach," Energies, MDPI, vol. 14(18), pages 1-24, September.
    11. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.
    12. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    13. Shab Gbémou & Julien Eynard & Stéphane Thil & Emmanuel Guillot & Stéphane Grieu, 2021. "A Comparative Study of Machine Learning-Based Methods for Global Horizontal Irradiance Forecasting," Energies, MDPI, vol. 14(11), pages 1-23, May.
    14. Alfredo Nespoli & Emanuele Ogliari & Sonia Leva & Alessandro Massi Pavan & Adel Mellit & Vanni Lughi & Alberto Dolara, 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques," Energies, MDPI, vol. 12(9), pages 1-15, April.
    15. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
    16. Munir Husein & Il-Yop Chung, 2019. "Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach," Energies, MDPI, vol. 12(10), pages 1-21, May.
    17. Hoyos-Gómez, Laura S. & Ruiz-Muñoz, Jose F. & Ruiz-Mendoza, Belizza J., 2022. "Short-term forecasting of global solar irradiance in tropical environments with incomplete data," Applied Energy, Elsevier, vol. 307(C).
    18. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    19. Majid Hosseini & Satya Katragadda & Jessica Wojtkiewicz & Raju Gottumukkala & Anthony Maida & Terrence Lynn Chambers, 2020. "Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 13(15), pages 1-15, July.
    20. Elena Collino & Dario Ronzio, 2021. "Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System," Energies, MDPI, vol. 14(3), pages 1-30, February.

    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:gam:jeners:v:13:y:2020:i:11:p:3005-:d:370022. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.