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Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information

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  1. Daxini, Rajiv & Sun, Yanyi & Wilson, Robin & Wu, Yupeng, 2022. "Direct spectral distribution characterisation using the Average Photon Energy for improved photovoltaic performance modelling," Renewable Energy, Elsevier, vol. 201(P1), pages 1176-1188.
  2. Yin, Wansi & Han, Yutong & Zhou, Hai & Ma, Ming & Li, Li & Zhu, Honglu, 2020. "A novel non-iterative correction method for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 159(C), pages 23-32.
  3. Li, Qing & Zhang, Xinyan & Ma, Tianjiao & Jiao, Chunlei & Wang, Heng & Hu, Wei, 2021. "A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine," Energy, Elsevier, vol. 224(C).
  4. Weifeng Xu & Bing Yu & Qing Song & Liguo Weng & Man Luo & Fan Zhang, 2022. "Economic and Low-Carbon-Oriented Distribution Network Planning Considering the Uncertainties of Photovoltaic Generation and Load Demand to Achieve Their Reliability," Energies, MDPI, vol. 15(24), pages 1-15, December.
  5. 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).
  6. Fei Mei & Yi Pan & Kedong Zhu & Jianyong Zheng, 2018. "A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
  7. Martins, Guilherme Santos & Giesbrecht, Mateus, 2023. "Hybrid approaches based on Singular Spectrum Analysis and k- Nearest Neighbors for clearness index forecasting," Renewable Energy, Elsevier, vol. 219(P1).
  8. 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.
  9. Xu, Lei & Hou, Lei & Zhu, Zhenyu & Li, Yu & Liu, Jiaquan & Lei, Ting & Wu, Xingguang, 2021. "Mid-term prediction of electrical energy consumption for crude oil pipelines using a hybrid algorithm of support vector machine and genetic algorithm," Energy, Elsevier, vol. 222(C).
  10. Zhao, Zhen-Yu & Chen, Yu-Long & Li, Heng, 2019. "What affects the development of renewable energy power generation projects in China: ISM analysis," Renewable Energy, Elsevier, vol. 131(C), pages 506-517.
  11. Zhu, Jiebei & Li, Mingrui & Luo, Lin & Zhang, Bidan & Cui, Mingjian & Yu, Lujie, 2023. "Short-term PV power forecast methodology based on multi-scale fluctuation characteristics extraction," Renewable Energy, Elsevier, vol. 208(C), pages 141-151.
  12. Nguyen, Thi Ngoc & Müsgens, Felix, 2022. "What drives the accuracy of PV output forecasts?," Applied Energy, Elsevier, vol. 323(C).
  13. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
  14. Su-Chang Lim & Jun-Ho Huh & Seok-Hoon Hong & Chul-Young Park & Jong-Chan Kim, 2022. "Solar Power Forecasting Using CNN-LSTM Hybrid Model," Energies, MDPI, vol. 15(21), pages 1-17, November.
  15. 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).
  16. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
  17. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
  18. 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).
  19. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
  20. Wentao Ma & Lihong Qiu & Fengyuan Sun & Sherif S. M. Ghoneim & Jiandong Duan, 2022. "PV Power Forecasting Based on Relevance Vector Machine with Sparrow Search Algorithm Considering Seasonal Distribution and Weather Type," Energies, MDPI, vol. 15(14), pages 1-24, July.
  21. Hong Wu & Haipeng Liu & Huaiping Jin & Yanping He, 2024. "Ultra-Short-Term Photovoltaic Power Prediction by NRGA-BiLSTM Considering Seasonality and Periodicity of Data," Energies, MDPI, vol. 17(18), pages 1-19, September.
  22. Wang, Yuhan & Zhang, Chu & Fu, Yongyan & Suo, Leiming & Song, Shihao & Peng, Tian & Shahzad Nazir, Muhammad, 2023. "Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization algorithm," Energy, Elsevier, vol. 280(C).
  23. Visser, Lennard & AlSkaif, Tarek & van Sark, Wilfried, 2022. "Operational day-ahead solar power forecasting for aggregated PV systems with a varying spatial distribution," Renewable Energy, Elsevier, vol. 183(C), pages 267-282.
  24. Luo, Xing & Zhang, Dongxiao & Zhu, Xu, 2022. "Combining transfer learning and constrained long short-term memory for power generation forecasting of newly-constructed photovoltaic plants," Renewable Energy, Elsevier, vol. 185(C), pages 1062-1077.
  25. 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.
  26. Haobo Fu & Ruizhuo Wang & Bingxu Zhai & Yuanzhuo Li & Pengyuan Li & Rui Zhang & Haoyuan He & Siyang Liao, 2025. "Data-Driven Proactive Early Warning of Grid Congestion Probability Based on Multiple Time Scales," Energies, MDPI, vol. 18(10), pages 1-17, May.
  27. Yue Guo & Yu Song & Zilong Lai & Xuyang Wang & Licheng Wang & Hui Qin, 2025. "Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Methods," Energies, MDPI, vol. 18(2), pages 1-17, January.
  28. Rita Teixeira & Adelaide Cerveira & Eduardo J. Solteiro Pires & José Baptista, 2024. "Advancing Renewable Energy Forecasting: A Comprehensive Review of Renewable Energy Forecasting Methods," Energies, MDPI, vol. 17(14), pages 1-30, July.
  29. Zhao, Xueyuan & Gao, Weijun & Qian, Fanyue & Ge, Jian, 2021. "Electricity cost comparison of dynamic pricing model based on load forecasting in home energy management system," Energy, Elsevier, vol. 229(C).
  30. Takahiro Takamatsu & Hideaki Ohtake & Takashi Oozeki, 2022. "Support Vector Quantile Regression for the Post-Processing of Meso-Scale Ensemble Prediction System Data in the Kanto Region: Solar Power Forecast Reducing Overestimation," Energies, MDPI, vol. 15(4), pages 1-18, February.
  31. Jincun Liu & Kangji Li & Wenping Xue, 2024. "Photovoltaic Solar Power Prediction Using iPSO-Based Data Clustering and AdaLSTM Network," Energies, MDPI, vol. 17(7), pages 1-21, March.
  32. Thi Ngoc Nguyen & Felix Musgens, 2021. "What drives the accuracy of PV output forecasts?," Papers 2111.02092, arXiv.org.
  33. Ding, Yuanping & Dang, Yaoguo, 2023. "Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model," Energy, Elsevier, vol. 277(C).
  34. 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).
  35. Ruijie Feng & Zhenshuo Guo & Xuan Meng & Chuanyu Sun, 2025. "Modeling and State of Charge Estimation of Vanadium Redox Flow Batteries: A Review," Energies, MDPI, vol. 18(17), pages 1-32, September.
  36. Habibnia, S. & Faraji, M. & Alizadeh, M.H. & Mollayousefi Zadeh, M. & Caire, R. & Gharehpetian, G.B. & Guerrero, J.M., 2026. "Data-driven solutions for microgrids energy management systems: A state-of-the-art survey on current trends and future directions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 228(C).
  37. Han, Youhua & Liu, Yang & Lu, Shixiang & Basalike, Pie & Zhang, Jili, 2021. "Electrical performance and power prediction of a roll-bond photovoltaic thermal array under dewing and frosting conditions," Energy, Elsevier, vol. 237(C).
  38. Jiahui Wang & Mingsheng Jia & Shishi Li & Kang Chen & Cheng Zhang & Xiuyu Song & Qianxi Zhang, 2024. "Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model," Sustainability, MDPI, vol. 16(7), pages 1-24, March.
  39. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  40. Wang, Hu & Mao, Lei & Zhang, Heng & Wu, Qiang, 2024. "Multi-prediction of electric load and photovoltaic solar power in grid-connected photovoltaic system using state transition method," Applied Energy, Elsevier, vol. 353(PB).
  41. Agüera-Pérez, Agustín & Palomares-Salas, José Carlos & González de la Rosa, Juan José & Florencias-Oliveros, Olivia, 2018. "Weather forecasts for microgrid energy management: Review, discussion and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 265-278.
  42. Spyros Theocharides & Marios Theristis & George Makrides & Marios Kynigos & Chrysovalantis Spanias & George E. Georghiou, 2021. "Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting," Energies, MDPI, vol. 14(4), pages 1-22, February.
  43. Zang, Haixiang & Cheng, Lilin & Ding, Tao & Cheung, Kwok W. & Wang, Miaomiao & Wei, Zhinong & Sun, Guoqiang, 2019. "Estimation and validation of daily global solar radiation by day of the year-based models for different climates in China," Renewable Energy, Elsevier, vol. 135(C), pages 984-1003.
  44. 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).
  45. 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).
  46. Obaid Aldosari & Salem Batiyah & Murtada Elbashir & Waleed Alhosaini & Kanagaraj Nallaiyagounder, 2024. "Performance Evaluation of Multiple Machine Learning Models in Predicting Power Generation for a Grid-Connected 300 MW Solar Farm," Energies, MDPI, vol. 17(2), pages 1-17, January.
  47. Sameer Al-Dahidi & Manoharan Madhiarasan & Loiy Al-Ghussain & Ahmad M. Abubaker & Adnan Darwish Ahmad & Mohammad Alrbai & Mohammadreza Aghaei & Hussein Alahmer & Ali Alahmer & Piero Baraldi & Enrico Z, 2024. "Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework," Energies, MDPI, vol. 17(16), pages 1-38, August.
  48. Chen, Xiang & Ding, Kun & Zhang, Jingwei & Han, Wei & Liu, Yongjie & Yang, Zenan & Weng, Shuai, 2022. "Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM," Energy, Elsevier, vol. 248(C).
  49. Md Mijanur Rahman & Mohammad Shakeri & Sieh Kiong Tiong & Fatema Khatun & Nowshad Amin & Jagadeesh Pasupuleti & Mohammad Kamrul Hasan, 2021. "Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks," Sustainability, MDPI, vol. 13(4), pages 1-28, February.
  50. Wen, Yan & Pan, Su & Li, Xinxin & Li, Zibo & Wen, Wuzhenghong, 2024. "Improving multi-site photovoltaic forecasting with relevance amplification: DeepFEDformer-based approach," Energy, Elsevier, vol. 299(C).
  51. Zhou, Daixuan & Liu, Yujin & Wang, Xu & Wang, Fuxing & Jia, Yan, 2025. "Combined ultra-short-term photovoltaic power prediction based on CEEMDAN decomposition and RIME optimized AM-TCN-BiLSTM," Energy, Elsevier, vol. 318(C).
  52. 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.
  53. Ridha, Hussein Mohammed & Ahmadipour, Masoud & Alghrairi, Mokhalad & Hizam, Hashim & Mirjalili, Seyedali & Zubaidi, Salah L. & Mohammed S, Marwa Y., 2026. "A novel hybrid photovoltaic current prediction model utilizing singular spectrum analysis, adaptive beluga whale optimization, and improved extreme learning machine," Renewable Energy, Elsevier, vol. 256(PA).
  54. Tawn, R. & Browell, J., 2022. "A review of very short-term wind and solar power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
  55. 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.
  56. Mateusz Sumorek & Adam Idzkowski, 2023. "Time Series Forecasting for Energy Production in Stand-Alone and Tracking Photovoltaic Systems Based on Historical Measurement Data," Energies, MDPI, vol. 16(17), pages 1-23, September.
  57. Zhang, Chu & Hua, Lei & Ji, Chunlei & Shahzad Nazir, Muhammad & Peng, Tian, 2022. "An evolutionary robust solar radiation prediction model based on WT-CEEMDAN and IASO-optimized outlier robust extreme learning machine," Applied Energy, Elsevier, vol. 322(C).
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