Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information
Author
Abstract
Suggested Citation
DOI: 10.1016/j.rser.2024.114691
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Wu, Xianguo & Liu, Huitao & Zhang, Limao & Skibniewski, Miroslaw J. & Deng, Qianli & Teng, Jiaying, 2015. "A dynamic Bayesian network based approach to safety decision support in tunnel construction," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 157-168.
- 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).
- Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
- 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.
- 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.
- 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).
- Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
- Zhang, Gang & Yang, Dazhi & Galanis, George & Androulakis, Emmanouil, 2022. "Solar forecasting with hourly updated numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
- Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
- Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
- Cai, Baoping & Liu, Yonghong & Fan, Qian & Zhang, Yunwei & Liu, Zengkai & Yu, Shilin & Ji, Renjie, 2014. "Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network," Applied Energy, Elsevier, vol. 114(C), pages 1-9.
- Zhang, Y. & Weng, W.G., 2020. "Bayesian network model for buried gas pipeline failure analysis caused by corrosion and external interference," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
- 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).
- Bertone, Edoardo & Sahin, Oz & Stewart, Rodney A. & Zou, Patrick X.W. & Alam, Morshed & Hampson, Keith & Blair, Evan, 2018. "Role of financial mechanisms for accelerating the rate of water and energy efficiency retrofits in Australian public buildings: Hybrid Bayesian Network and System Dynamics modelling approach," Applied Energy, Elsevier, vol. 210(C), pages 409-419.
- Li, Tingting & Zhou, Yangze & Zhao, Yang & Zhang, Chaobo & Zhang, Xuejun, 2022. "A hierarchical object oriented Bayesian network-based fault diagnosis method for building energy systems," Applied Energy, Elsevier, vol. 306(PB).
- Amin, Md. Tanjin & Khan, Faisal & Imtiaz, Syed, 2018. "Dynamic availability assessment of safety critical systems using a dynamic Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 108-117.
- Wang, Lining & Mao, Mingxuan & Xie, Jili & Liao, Zheng & Zhang, Hao & Li, Huanxin, 2023. "Accurate solar PV power prediction interval method based on frequency-domain decomposition and LSTM model," Energy, Elsevier, vol. 262(PB).
- Ayvazoğluyüksel, Özge & Filik, Ümmühan Başaran, 2018. "Estimation methods of global solar radiation, cell temperature and solar power forecasting: A review and case study in Eskişehir," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 639-653.
- Tang, Kayu & Parsons, David J. & Jude, Simon, 2019. "Comparison of automatic and guided learning for Bayesian networks to analyse pipe failures in the water distribution system," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 24-36.
- Long, Huan & Zhang, Zijun & Su, Yan, 2014. "Analysis of daily solar power prediction with data-driven approaches," Applied Energy, Elsevier, vol. 126(C), pages 29-37.
- Rai, Amit & Shrivastava, Ashish & Jana, Kartick C., 2023. "Differential attention net: Multi-directed differential attention based hybrid deep learning model for solar power forecasting," Energy, Elsevier, vol. 263(PC).
- 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.
- Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
- 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.
- Barbieri, Florian & Rajakaruna, Sumedha & Ghosh, Arindam, 2017. "Very short-term photovoltaic power forecasting with cloud modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 242-263.
- Quintanar-Gago, David A. & Nelson, Pamela F. & DÃaz-Sánchez, à ngeles & Boldrick, Michael S., 2021. "Assessment of steam turbine blade failure and damage mechanisms using a Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
- Borunda, Mónica & Jaramillo, O.A. & Reyes, Alberto & Ibargüengoytia, Pablo H., 2016. "Bayesian networks in renewable energy systems: A bibliographical survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 32-45.
- Polleux, Louis & Guerassimoff, Gilles & Marmorat, Jean-Paul & Sandoval-Moreno, John & Schuhler, Thierry, 2022. "An overview of the challenges of solar power integration in isolated industrial microgrids with reliability constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
- Zhang, Qiongfang & Xu, Nan & Ersoy, Daniel & Liu, Yongming, 2022. "Manifold-based Conditional Bayesian network for aging pipe yield strength estimation with non-destructive measurements," Reliability Engineering and System Safety, Elsevier, vol. 223(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.- 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.
- 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).
- Yang, Yanru & Liu, Yu & Zhang, Yihang & Shu, Shaolong & Zheng, Junsheng, 2025. "DEST-GNN: A double-explored spatio-temporal graph neural network for multi-site intra-hour PV power forecasting," Applied Energy, Elsevier, vol. 378(PA).
- 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.
- Nguyen, Thi Ngoc & Müsgens, Felix, 2022. "What drives the accuracy of PV output forecasts?," Applied Energy, Elsevier, vol. 323(C).
- 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).
- Kim, Jimin & Obregon, Josue & Park, Hoonseok & Jung, Jae-Yoon, 2024. "Multi-step photovoltaic power forecasting using transformer and recurrent neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
- Nunes Maciel, Joylan & Javier Gimenez Ledesma, Jorge & Hideo Ando Junior, Oswaldo, 2024. "Hybrid prediction method of solar irradiance applied to short-term photovoltaic energy generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
- Sahu, Atma Ram & Palei, Sanjay Kumar, 2022. "Fault analysis of dragline subsystem using Bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
- 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).
- 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).
- Wang, Min & Rao, Congjun & Xiao, Xinping & Hu, Zhuo & Goh, Mark, 2024. "Efficient shrinkage temporal convolutional network model for photovoltaic power prediction," Energy, Elsevier, vol. 297(C).
- 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).
- Rai, Amit & Shrivastava, Ashish & Jana, Kartick C., 2023. "Differential attention net: Multi-directed differential attention based hybrid deep learning model for solar power forecasting," Energy, Elsevier, vol. 263(PC).
- Tahir, Muhammad Faizan & Yousaf, Muhammad Zain & Tzes, Anthony & El Moursi, Mohamed Shawky & El-Fouly, Tarek H.M., 2024. "Enhanced solar photovoltaic power prediction using diverse machine learning algorithms with hyperparameter optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
- Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
- 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.
- Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).
- Konduru Sudharshan & C. Naveen & Pradeep Vishnuram & Damodhara Venkata Siva Krishna Rao Kasagani & Benedetto Nastasi, 2022. "Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction," Energies, MDPI, vol. 15(17), pages 1-39, August.
- Nie, Yuhao & Li, Xiatong & Paletta, Quentin & Aragon, Max & Scott, Andea & Brandt, Adam, 2024. "Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
More about this item
Keywords
PV power forecasting; Dynamic bayesian network; Information fusion; Causal interpretation; Solar plants;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:eee:rensus:v:202:y:2024:i:c:s1364032124004179. 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/600126/description#description .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.