IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v108y2017icp635-643.html
   My bibliography  Save this article

A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting

Author

Listed:
  • Hocaoglu, Fatih Onur
  • Serttas, Fatih

Abstract

Short term solar radiation forecasting is significant for the operation of big installations and the electrical grid. There are many short term forecasting techniques in the literature. In this study, short term predictions of solar radiation are reviewed. An alternative approach and model is proposed. The approach assumes that solar radiation data repeats itself in the history. According to this initial assumption, a novel Mycielski based model is proposed. This model considers the recorded hourly solar radiation data as an array and starting from the last record value, it tries to find most similar sub-array pattern in the history. This sub-array pattern corresponds to the longest matching solar radiation data array in the history. The data observed after this longest array in history is considered as the prediction. In case several sub-arrays are obtained, the model decides the selection according to the probabilistic relations of the sub-patterns last values to the next value. To model the probabilistic relations of the data, a Markov chain model is adopted and used. By this way historical search model is strengthened. Thanks to the proposed model, accurate predictions are obtained. To show the effectiveness, the results are compared with the models of others in the literature as a final task.

Suggested Citation

  • Hocaoglu, Fatih Onur & Serttas, Fatih, 2017. "A novel hybrid (Mycielski-Markov) model for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 635-643.
    • Handle: RePEc:eee:renene:v:108:y:2017:i:c:p:635-643
    DOI: 10.1016/j.renene.2016.08.058
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116307686
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2016.08.058?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Furlan, Claudia & de Oliveira, Amauri Pereira & Soares, Jacyra & Codato, Georgia & Escobedo, João Francisco, 2012. "The role of clouds in improving the regression model for hourly values of diffuse solar radiation," Applied Energy, Elsevier, vol. 92(C), pages 240-254.
    2. Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
    3. Zjavka, Ladislav, 2015. "Wind speed forecast correction models using polynomial neural networks," Renewable Energy, Elsevier, vol. 83(C), pages 998-1006.
    4. Hervás Soriano, Fernando & Mulatero, Fulvio, 2011. "EU Research and Innovation (R&I) in renewable energies: The role of the Strategic Energy Technology Plan (SET-Plan)," Energy Policy, Elsevier, vol. 39(6), pages 3582-3590, June.
    5. Kashyap, Yashwant & Bansal, Ankit & Sao, Anil K., 2015. "Solar radiation forecasting with multiple parameters neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 825-835.
    6. Zhang, Xiongwen, 2014. "A statistical approach for sub-hourly solar radiation reconstruction," Renewable Energy, Elsevier, vol. 71(C), pages 307-314.
    7. Mellit, A. & Sağlam, S. & Kalogirou, S.A., 2013. "Artificial neural network-based model for estimating the produced power of a photovoltaic module," Renewable Energy, Elsevier, vol. 60(C), pages 71-78.
    8. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Nissen, J.N., 2015. "A novel application of an analog ensemble for short-term wind power forecasting," Renewable Energy, Elsevier, vol. 76(C), pages 768-781.
    9. Evans, S.P. & Clausen, P.D., 2015. "Modelling of turbulent wind flow using the embedded Markov chain method," Renewable Energy, Elsevier, vol. 81(C), pages 671-678.
    10. 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.
    11. Akarslan, Emre & Hocaoğlu, Fatih Onur & Edizkan, Rifat, 2014. "A novel M-D (multi-dimensional) linear prediction filter approach for hourly solar radiation forecasting," Energy, Elsevier, vol. 73(C), pages 978-986.
    12. Batlles, F.J. & Rubio, M.A. & Tovar, J. & Olmo, F.J. & Alados-Arboledas, L., 2000. "Empirical modeling of hourly direct irradiance by means of hourly global irradiance," Energy, Elsevier, vol. 25(7), pages 675-688.
    13. Akarslan, Emre & Hocaoglu, Fatih Onur, 2016. "A novel adaptive approach for hourly solar radiation forecasting," Renewable Energy, Elsevier, vol. 87(P1), pages 628-633.
    14. Wang, Jianzhou & Qin, Shanshan & Jin, Shiqiang & Wu, Jie, 2015. "Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 26-42.
    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. Adel Alblawi & M. H. Elkholy & M. Talaat, 2019. "ANN for Assessment of Energy Consumption of 4 kW PV Modules over a Year Considering the Impacts of Temperature and Irradiance," Sustainability, MDPI, vol. 11(23), pages 1-24, November.
    2. Mari R. Tye & Sue Ellen Haupt & Eric Gilleland & Christina Kalb & Tara Jensen, 2019. "Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait," Energies, MDPI, vol. 12(23), pages 1-17, November.
    3. Eduardo Rangel-Heras & César Angeles-Camacho & Erasmo Cadenas-Calderón & Rafael Campos-Amezcua, 2022. "Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model," Energies, MDPI, vol. 15(8), pages 1-23, April.
    4. 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.
    5. Julián Urrego-Ortiz & J. Alejandro Martínez & Paola A. Arias & Álvaro Jaramillo-Duque, 2019. "Assessment and Day-Ahead Forecasting of Hourly Solar Radiation in Medellín, Colombia," Energies, MDPI, vol. 12(22), pages 1-29, November.
    6. Hassan, Muhammed A. & Al-Ghussain, Loiy & Ahmad, Adnan Darwish & Abubaker, Ahmad M. & Khalil, Adel, 2022. "Aggregated independent forecasters of half-hourly global horizontal irradiance," Renewable Energy, Elsevier, vol. 181(C), pages 365-383.
    7. Wang, Jianxing & Guo, Lili & Zhang, Chengying & Song, Lei & Duan, Jiangyong & Duan, Liqiang, 2020. "Thermal power forecasting of solar power tower system by combining mechanism modeling and deep learning method," Energy, Elsevier, vol. 208(C).
    8. Zhang, Lanchuan & Li, Jun & Meng, Junhui & Du, Huafei & Lv, Mingyun & Zhu, Weiyu, 2018. "Thermal performance analysis of a high-altitude solar-powered hybrid airship," Renewable Energy, Elsevier, vol. 125(C), pages 890-906.
    9. Jiang, Hou & Lu, Ning & Huang, Guanghui & Yao, Ling & Qin, Jun & Liu, Hengzi, 2020. "Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data," Applied Energy, Elsevier, vol. 270(C).
    10. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    11. Zhang, Lanchuan & Li, Jun & Wu, Yifei & Lv, Mingyun, 2019. "Analysis of attitude planning and energy balance of stratospheric airship," Energy, Elsevier, vol. 183(C), pages 1089-1103.
    12. Martins, Guilherme Santos & Giesbrecht, Mateus, 2021. "Clearness index forecasting: A comparative study between a stochastic realization method and a machine learning algorithm," Renewable Energy, Elsevier, vol. 180(C), pages 787-805.
    13. Ahmed Aljanad & Nadia M. L. Tan & Vassilios G. Agelidis & Hussain Shareef, 2021. "Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-20, February.
    14. Zhou, Qingguo & Wang, Chen & Zhang, Gaofeng, 2019. "Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems," Applied Energy, Elsevier, vol. 250(C), pages 1559-1580.
    15. Shang, Chuanfu & Wei, Pengcheng, 2018. "Enhanced support vector regression based forecast engine to predict solar power output," Renewable Energy, Elsevier, vol. 127(C), pages 269-283.
    16. Bempah, Kwabena Opoku & Kwon, Kyoungjun & Kim, Katherine A., 2019. "Experimental study of photovoltaic panel mounting configurations for tube-shaped structures," Applied Energy, Elsevier, vol. 240(C), pages 754-765.
    17. Ruan, Zhaohui & Sun, Weiwei & Yuan, Yuan & Tan, Heping, 2023. "Accurately forecasting solar radiation distribution at both spatial and temporal dimensions simultaneously with fully-convolutional deep neural network model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    18. Heng, Jiani & Wang, Jianzhou & Xiao, Liye & Lu, Haiyan, 2017. "Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting," Applied Energy, Elsevier, vol. 208(C), pages 845-866.
    19. Ramírez, Andres Felipe & Valencia, Carlos Felipe & Cabrales, Sergio & Ramírez, Carlos G., 2021. "Simulation of photo-voltaic power generation using copula autoregressive models for solar irradiance and air temperature time series," Renewable Energy, Elsevier, vol. 175(C), pages 44-67.
    20. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    21. Akarslan, Emre & Hocaoglu, Fatih Onur & Edizkan, Rifat, 2018. "Novel short term solar irradiance forecasting models," Renewable Energy, Elsevier, vol. 123(C), pages 58-66.
    22. Xinyuan Hou & Kyriakoula Papachristopoulou & Yves-Marie Saint-Drenan & Stelios Kazadzis, 2022. "Solar Radiation Nowcasting Using a Markov Chain Multi-Model Approach," Energies, MDPI, vol. 15(9), pages 1-24, April.
    23. 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).
    24. Song, Xiaodong & Johnson, Paul & Duck, Peter, 2021. "A novel combination of Mycielski–Markov, regime switching and jump diffusion models for solar energy," Applied Energy, Elsevier, vol. 301(C).
    25. Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.

    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. Fatemi, Seyyed A. & Kuh, Anthony & Fripp, Matthias, 2018. "Parametric methods for probabilistic forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 129(PA), pages 666-676.
    2. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2015. "Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1093-1106.
    3. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    4. Akarslan, Emre & Hocaoglu, Fatih Onur & Edizkan, Rifat, 2018. "Novel short term solar irradiance forecasting models," Renewable Energy, Elsevier, vol. 123(C), pages 58-66.
    5. Murthy, K.S.R. & Rahi, O.P., 2017. "A comprehensive review of wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1320-1342.
    6. Monika Zielińska-Sitkiewicz & Mariola Chrzanowska & Konrad Furmańczyk & Kacper Paczutkowski, 2021. "Analysis of Electricity Consumption in Poland Using Prediction Models and Neural Networks," Energies, MDPI, vol. 14(20), pages 1-21, October.
    7. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    8. Wang, Yong & He, Xinbo & Zhou, Ying & Luo, Yongxian & Tang, Yanbing & Narayanan, Govindasami, 2024. "A novel structure adaptive grey seasonal model with data reorganization and its application in solar photovoltaic power generation prediction," Energy, Elsevier, vol. 302(C).
    9. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    10. Paiho, Satu & Kiljander, Jussi & Sarala, Roope & Siikavirta, Hanne & Kilkki, Olli & Bajpai, Arpit & Duchon, Markus & Pahl, Marc-Oliver & Wüstrich, Lars & Lübben, Christian & Kirdan, Erkin & Schindler,, 2021. "Towards cross-commodity energy-sharing communities – A review of the market, regulatory, and technical situation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    11. Fatemi, Seyyed A. & Kuh, Anthony & Fripp, Matthias, 2016. "Online and batch methods for solar radiation forecast under asymmetric cost functions," Renewable Energy, Elsevier, vol. 91(C), pages 397-408.
    12. Akarslan, Emre & Hocaoglu, Fatih Onur, 2017. "A novel method based on similarity for hourly solar irradiance forecasting," Renewable Energy, Elsevier, vol. 112(C), pages 337-346.
    13. Kermadi, Mostefa & Berkouk, El Madjid, 2017. "Artificial intelligence-based maximum power point tracking controllers for Photovoltaic systems: Comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 369-386.
    14. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    15. Lilin Cheng & Haixiang Zang & Tao Ding & Rong Sun & Miaomiao Wang & Zhinong Wei & Guoqiang Sun, 2018. "Ensemble Recurrent Neural Network Based Probabilistic Wind Speed Forecasting Approach," Energies, MDPI, vol. 11(8), pages 1-23, July.
    16. Kyriakopoulos, Grigorios L. & Arabatzis, Garyfallos & Tsialis, Panagiotis & Ioannou, Konstantinos, 2018. "Electricity consumption and RES plants in Greece: Typologies of regional units," Renewable Energy, Elsevier, vol. 127(C), pages 134-144.
    17. Yuyang Gao & Chao Qu & Kequan Zhang, 2016. "A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting," Energies, MDPI, vol. 9(10), pages 1-28, September.
    18. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    19. Hemmatabady, Hoofar & Welsch, Bastian & Formhals, Julian & Sass, Ingo, 2022. "AI-based enviro-economic optimization of solar-coupled and standalone geothermal systems for heating and cooling," Applied Energy, Elsevier, vol. 311(C).
    20. Selimefendigil, Fatih & Öztop, Hakan F., 2020. "Identification of pulsating flow effects with CNT nanoparticles on the performance enhancements of thermoelectric generator (TEG) module in renewable energy applications," Renewable Energy, Elsevier, vol. 162(C), pages 1076-1086.

    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:eee:renene:v:108:y:2017:i:c:p:635-643. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.