IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v312y2022ics0306261922001830.html
   My bibliography  Save this article

Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm

Author

Listed:
  • Wang, Jianzhou
  • Zhou, Yilin
  • Li, Zhiwu

Abstract

As the penetration rate of solar energy in the grid continues to enhance, solar power photovoltaic generation forecasts have become an indispensable aspect of mechanism mobilization and maintenance of the stability of the power system. In this regard, many researchers have done a lot of study, and put forward some predictive models. However, many individual prediction systems only consider the prediction accuracy rate without further considering the prediction utility and stability. To fill this gap, a comprehensive system is designed in this paper, which is on the basis of automatic optimization of variational mode decomposition mechanism, and the weight of system is determined by multi objective intelligent optimization algorithm. In particular, it can be proved theoretically that the developed predictive system can achieve the pareto optimal solution. And the designed system is shown to be very effective in forecasting the 2021 photovoltaic power data obtained from Belgium. The empirical study reports that the combination of variational mode decomposition strategy based on genetic algorithm and multi objective grasshopper optimization algorithm is found to be the satisfactory strategy to optimize the predictive system compared with other common mechanism. And the results of several numerical studies show that the designed predictive system achieves the superior performance as compared to the control systems, and in multi-step forecasting, the designed system has better stability than the comparison systems.

Suggested Citation

  • Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922001830
    DOI: 10.1016/j.apenergy.2022.118725
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922001830
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.118725?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. Li, Chengdong & Zhou, Changgeng & Peng, Wei & Lv, Yisheng & Luo, Xin, 2020. "Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method," Energy, Elsevier, vol. 212(C).
    2. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    3. 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.
    4. Niu, Xinsong & Wang, Jiyang, 2019. "A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 241(C), pages 519-539.
    5. Wang, Jianzhou & Jiang, He & Wu, Yujie & Dong, Yao, 2015. "Forecasting solar radiation using an optimized hybrid model by Cuckoo Search algorithm," Energy, Elsevier, vol. 81(C), pages 627-644.
    6. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    7. Eseye, Abinet Tesfaye & Zhang, Jianhua & Zheng, Dehua, 2018. "Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information," Renewable Energy, Elsevier, vol. 118(C), pages 357-367.
    8. Ben Ammar, Rim & Ben Ammar, Mohsen & Oualha, Abdelmajid, 2020. "Photovoltaic power forecast using empirical models and artificial intelligence approaches for water pumping systems," Renewable Energy, Elsevier, vol. 153(C), pages 1016-1028.
    9. Gao, Bixuan & Huang, Xiaoqiao & Shi, Junsheng & Tai, Yonghang & Zhang, Jun, 2020. "Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 1665-1683.
    10. Moreira, M.O. & Balestrassi, P.P. & Paiva, A.P. & Ribeiro, P.F. & Bonatto, B.D., 2021. "Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    11. Darvish Falehi, Ali, 2020. "An innovative optimal RPO-FOSMC based on multi-objective grasshopper optimization algorithm for DFIG-based wind turbine to augment MPPT and FRT capabilities," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
    12. 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.
    13. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
    14. Bemani, Amin & Xiong, Qingang & Baghban, Alireza & Habibzadeh, Sajjad & Mohammadi, Amir H. & Doranehgard, Mohammad Hossein, 2020. "Modeling of cetane number of biodiesel from fatty acid methyl ester (FAME) information using GA-, PSO-, and HGAPSO- LSSVM models," Renewable Energy, Elsevier, vol. 150(C), pages 924-934.
    15. Kong, Weicong & Jia, Youwei & Dong, Zhao Yang & Meng, Ke & Chai, Songjian, 2020. "Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting," Applied Energy, Elsevier, vol. 280(C).
    16. Gao, Mingming & Li, Jianjing & Hong, Feng & Long, Dongteng, 2019. "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, Elsevier, vol. 187(C).
    17. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    18. 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.
    19. 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).
    20. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
    21. Abbassi, Abdelkader & Dami, Mohamed Ali & Jemli, Mohamed, 2017. "A statistical approach for hybrid energy storage system sizing based on capacity distributions in an autonomous PV/Wind power generation system," Renewable Energy, Elsevier, vol. 103(C), pages 81-93.
    22. 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.
    23. Zhilong Wang & Chen Wang & Jie Wu, 2016. "Wind Energy Potential Assessment and Forecasting Research Based on the Data Pre-Processing Technique and Swarm Intelligent Optimization Algorithms," Sustainability, MDPI, vol. 8(11), pages 1-32, November.
    24. Wang, Jianzhou & Jiang, Haiyan & Zhou, Qingping & Wu, Jie & Qin, Shanshan, 2016. "China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1149-1167.
    25. Lee, Donghun & Kim, Kwanho, 2021. "PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information," Renewable Energy, Elsevier, vol. 173(C), pages 1098-1110.
    26. 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).
    27. 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.
    28. 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.
    29. Jamal, Taskin & Carter, Craig & Schmidt, Thomas & Shafiullah, G.M. & Calais, Martina & Urmee, Tania, 2019. "An energy flow simulation tool for incorporating short-term PV forecasting in a diesel-PV-battery off-grid power supply system," Applied Energy, Elsevier, vol. 254(C).
    30. Kushwaha, Vishal & Pindoriya, Naran M., 2019. "A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast," Renewable Energy, Elsevier, vol. 140(C), pages 124-139.
    31. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
    32. Wu, Yujie & Wang, Jianzhou, 2016. "A novel hybrid model based on artificial neural networks for solar radiation prediction," Renewable Energy, Elsevier, vol. 89(C), pages 268-284.
    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. Tian, Zhirui & Gai, Mei, 2023. "A novel hybrid wind speed prediction framework based on multi-strategy improved optimizer and new data pre-processing system with feedback mechanism," Energy, Elsevier, vol. 281(C).
    2. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & Jesus Lopez-Sotelo & David Celeita, 2023. "An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture," Energies, MDPI, vol. 16(19), pages 1-24, September.
    3. Max Olinto Moreira & Betania Mafra Kaizer & Takaaki Ohishi & Benedito Donizeti Bonatto & Antonio Carlos Zambroni de Souza & Pedro Paulo Balestrassi, 2022. "Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting," Energies, MDPI, vol. 16(1), pages 1-30, December.
    4. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
    5. Zhi, Yuan & Yang, Xudong, 2023. "Scenario-based multi-objective optimization strategy for rural PV-battery systems," Applied Energy, Elsevier, vol. 345(C).
    6. Wang, Jianzhou & An, Yining & Li, Zhiwu & Lu, Haiyan, 2022. "A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting," Energy, Elsevier, vol. 251(C).
    7. Bo Gu & Xi Li & Fengliang Xu & Xiaopeng Yang & Fayi Wang & Pengzhan Wang, 2023. "Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    8. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).
    9. Wang, Jianzhou & Niu, Xinsong & Zhang, Lifang & Liu, Zhenkun & Wei, Danxiang, 2022. "The influence of international oil prices on the exchange rates of oil exporting countries: Based on the hybrid copula function," Resources Policy, Elsevier, vol. 77(C).

    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. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    2. Nguyen, Thi Ngoc & Müsgens, Felix, 2022. "What drives the accuracy of PV output forecasts?," Applied Energy, Elsevier, vol. 323(C).
    3. Sarmas, Elissaios & Spiliotis, Evangelos & Stamatopoulos, Efstathios & Marinakis, Vangelis & Doukas, Haris, 2023. "Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models," Renewable Energy, Elsevier, vol. 216(C).
    4. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    5. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Zhang, Jun & Shi, Junsheng & Gao, Bixuan & Liu, Wuming, 2021. "Hybrid deep neural model for hourly solar irradiance forecasting," Renewable Energy, Elsevier, vol. 171(C), pages 1041-1060.
    6. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
    7. Korkmaz, Deniz, 2021. "SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 300(C).
    8. Xu, Fang Yuan & Tang, Rui Xin & Xu, Si Bin & Fan, Yi Liang & Zhou, Ya & Zhang, Hao Tian, 2021. "Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification," Energy, Elsevier, vol. 223(C).
    9. Zhen, Hao & Niu, Dongxiao & Wang, Keke & Shi, Yucheng & Ji, Zhengsen & Xu, Xiaomin, 2021. "Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information," Energy, Elsevier, vol. 231(C).
    10. 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.
    11. Minli Wang & Peihong Wang & Tao Zhang, 2022. "Evidential Extreme Learning Machine Algorithm-Based Day-Ahead Photovoltaic Power Forecasting," Energies, MDPI, vol. 15(11), pages 1-25, May.
    12. 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).
    13. Medine Colak & Mehmet Yesilbudak & Ramazan Bayindir, 2020. "Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information," Energies, MDPI, vol. 13(4), pages 1-19, February.
    14. Yuan An & Kaikai Dang & Xiaoyu Shi & Rong Jia & Kai Zhang & Qiang Huang, 2021. "A Probabilistic Ensemble Prediction Method for PV Power in the Nonstationary Period," Energies, MDPI, vol. 14(4), pages 1-18, February.
    15. Thi Ngoc Nguyen & Felix Musgens, 2021. "What drives the accuracy of PV output forecasts?," Papers 2111.02092, arXiv.org.
    16. Putri Nor Liyana Mohamad Radzi & Muhammad Naveed Akhter & Saad Mekhilef & Noraisyah Mohamed Shah, 2023. "Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    17. du Plessis, A.A. & Strauss, J.M. & Rix, A.J., 2021. "Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour," Applied Energy, Elsevier, vol. 285(C).
    18. 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.
    19. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    20. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).

    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:appene:v:312:y:2022:i:c:s0306261922001830. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.