Solar Photovoltaic Power Prediction Using Big Data Tools
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
- Li, Yanting & Su, Yan & Shu, Lianjie, 2014. "An ARMAX model for forecasting the power output of a grid connected photovoltaic system," Renewable Energy, Elsevier, vol. 66(C), pages 78-89.
- Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
- 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.
- Claudio Monteiro & Tiago Santos & L. Alfredo Fernandez-Jimenez & Ignacio J. Ramirez-Rosado & M. Sonia Terreros-Olarte, 2013. "Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity," Energies, MDPI, vol. 6(5), pages 1-20, May.
- Maghami, Mohammad Reza & Hizam, Hashim & Gomes, Chandima & Radzi, Mohd Amran & Rezadad, Mohammad Ismael & Hajighorbani, Shahrooz, 2016. "Power loss due to soiling on solar panel: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1307-1316.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Ali Kamil Gumar & Funda Demir, 2022. "Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks," Energies, MDPI, vol. 15(22), pages 1-15, November.
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.- Mariz B. Arias & Sungwoo Bae, 2020. "Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System," Energies, MDPI, vol. 13(9), pages 1-14, April.
- 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.
- Lan, Hai & Zhang, Chi & Hong, Ying-Yi & He, Yin & Wen, Shuli, 2019. "Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network," Applied Energy, Elsevier, vol. 247(C), pages 389-402.
- Mohammed A. Bou-Rabee & Muhammad Yasin Naz & Imad ED. Albalaa & Shaharin Anwar Sulaiman, 2022. "BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones," Energies, MDPI, vol. 15(6), pages 1-12, March.
- 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.
- 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.
- Dukhwan Yu & Wonik Choi & Myoungsoo Kim & Ling Liu, 2020. "Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory," Energies, MDPI, vol. 13(15), pages 1-17, August.
- 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).
- Hanifi, Hamed & Pander, Matthias & Zeller, Ulli & Ilse, Klemens & Dassler, David & Mirza, Mark & Bahattab, Mohammed A. & Jaeckel, Bengt & Hagendorf, Christian & Ebert, Matthias & Gottschalg, Ralph & S, 2020. "Loss analysis and optimization of PV module components and design to achieve higher energy yield and longer service life in desert regions," Applied Energy, Elsevier, vol. 280(C).
- Duggal, Angel Swastik & Singh, Rajesh & Gehlot, Anita & Gupta, Lovi Raj & Akram, Sheik Vaseem & Prakash, Chander & Singh, Sunpreet & Kumar, Raman, 2021. "Infrastructure, mobility and safety 4.0: Modernization in road transportation," Technology in Society, Elsevier, vol. 67(C).
- Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
- Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
- Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
- Fabiana Lisco & Farwah Bukhari & Soňa Uličná & Kenan Isbilir & Kurt L. Barth & Alan Taylor & John M. Walls, 2020. "Degradation of Hydrophobic, Anti-Soiling Coatings for Solar Module Cover Glass," Energies, MDPI, vol. 13(15), pages 1-15, July.
- Mostafa. F. Shaaban & Amal Alarif & Mohamed Mokhtar & Usman Tariq & Ahmed H. Osman & A. R. Al-Ali, 2020. "A New Data-Based Dust Estimation Unit for PV Panels," Energies, MDPI, vol. 13(14), pages 1-17, July.
- Aurelia Rybak & Aleksandra Rybak & Spas D. Kolev, 2023. "Modeling the Photovoltaic Power Generation in Poland in the Light of PEP2040: An Application of Multiple Regression," Energies, MDPI, vol. 16(22), pages 1-17, November.
- Ghaith, Ahmad F. & Epplin, Francis M. & Frazier, R. Scott, 2017. "Economics of grid-tied household solar panel systems versus grid-only electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 407-424.
- Kuk Yeol Bae & Han Seung Jang & Bang Chul Jung & Dan Keun Sung, 2019. "Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-20, April.
- Wassim Salameh & Jalal Faraj & Elias Harika & Rabih Murr & Mahmoud Khaled, 2021. "On the Optimization of Electrical Water Heaters: Modelling Simulations and Experimentation," Energies, MDPI, vol. 14(13), pages 1-12, June.
- Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
More about this item
Keywords
big data tools; solar irradiance; solar PV power prediction model; weather data;All these keywords.
Statistics
Access and download statisticsCorrections
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:jsusta:v:13:y:2021:i:24:p:13685-:d:699877. 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.