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Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms

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Cited by:

  1. Vishnu Suresh & Przemyslaw Janik & Jacek Rezmer & Zbigniew Leonowicz, 2020. "Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm," Energies, MDPI, vol. 13(3), pages 1-15, February.
  2. Hassan, Muhammed A. & Al-Ghussain, Loiy & Khalil, Adel & Kaseb, Sayed A., 2022. "Self-calibrated hybrid weather forecasters for solar thermal and photovoltaic power plants," Renewable Energy, Elsevier, vol. 188(C), pages 1120-1140.
  3. Guosheng Duan & Lifeng Wu & Fa Liu & Yicheng Wang & Shaofei Wu, 2022. "Improvement in Solar-Radiation Forecasting Based on Evolutionary KNEA Method and Numerical Weather Prediction," Sustainability, MDPI, vol. 14(11), pages 1-20, June.
  4. Athanasios I. Salamanis & Georgia Xanthopoulou & Napoleon Bezas & Christos Timplalexis & Angelina D. Bintoudi & Lampros Zyglakis & Apostolos C. Tsolakis & Dimosthenis Ioannidis & Dionysios Kehagias & , 2020. "Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting," Energies, MDPI, vol. 13(22), pages 1-31, November.
  5. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
  6. Gairaa, Kacem & Voyant, Cyril & Notton, Gilles & Benkaciali, Saïd & Guermoui, Mawloud, 2022. "Contribution of ordinal variables to short-term global solar irradiation forecasting for sites with low variabilities," Renewable Energy, Elsevier, vol. 183(C), pages 890-902.
  7. Kong, Xiangfei & Du, Xinyu & Xue, Guixiang & Xu, Zhijie, 2023. "Multi-step short-term solar radiation prediction based on empirical mode decomposition and gated recurrent unit optimized via an attention mechanism," Energy, Elsevier, vol. 282(C).
  8. 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).
  9. Yeom, Jong-Min & Deo, Ravinesh C. & Adamwoski, Jan F. & Chae, Taebyeong & Kim, Dong-Su & Han, Kyung-Soo & Kim, Do-Yong, 2020. "Exploring solar and wind energy resources in North Korea with COMS MI geostationary satellite data coupled with numerical weather prediction reanalysis variables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 119(C).
  10. Vladimir Simankov & Pavel Buchatskiy & Anatoliy Kazak & Semen Teploukhov & Stefan Onishchenko & Kirill Kuzmin & Petr Chetyrbok, 2024. "A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies," Energies, MDPI, vol. 17(2), pages 1-23, January.
  11. 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).
  12. 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.
  13. Lim, Juin Yau & Safder, Usman & How, Bing Shen & Ifaei, Pouya & Yoo, Chang Kyoo, 2021. "Nationwide sustainable renewable energy and Power-to-X deployment planning in South Korea assisted with forecasting model," Applied Energy, Elsevier, vol. 283(C).
  14. 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.
  15. Acikgoz, Hakan, 2022. "A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting," Applied Energy, Elsevier, vol. 305(C).
  16. Xu, Shaozhen & Liu, Jun & Huang, Xiaoqiao & Li, Chengli & Chen, Zaiqing & Tai, Yonghang, 2024. "Minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement," Renewable Energy, Elsevier, vol. 224(C).
  17. Wang, Yun & Song, Mengmeng & Yang, Dazhi, 2024. "Local-global feature-based spatio-temporal wind speed forecasting with a sparse and dynamic graph," Energy, Elsevier, vol. 289(C).
  18. Tserenpurev Chuluunsaikhan & Jeong-Hun Kim & Yoonsung Shin & Sanghyun Choi & Aziz Nasridinov, 2022. "Feasibility Study on the Influence of Data Partition Strategies on Ensemble Deep Learning: The Case of Forecasting Power Generation in South Korea," Energies, MDPI, vol. 15(20), pages 1-20, October.
  19. Pedregal, Diego J. & Trapero, Juan R., 2021. "Adjusted combination of moving averages: A forecasting system for medium-term solar irradiance," Applied Energy, Elsevier, vol. 298(C).
  20. Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2023. "Assessment of solar radiation resource and photovoltaic power potential across China based on optimized interpretable machine learning model and GIS-based approaches," Applied Energy, Elsevier, vol. 339(C).
  21. Dou, Weijing & Wang, Kai & Shan, Shuo & Li, Chenxi & Wang, Yiye & Zhang, Kanjian & Wei, Haikun & Sreeram, Victor, 2024. "Day-ahead Numerical Weather Prediction solar irradiance correction using a clustering method based on weather conditions," Applied Energy, Elsevier, vol. 365(C).
  22. Vu, Ba Hau & Chung, Il-Yop, 2022. "Optimal generation scheduling and operating reserve management for PV generation using RNN-based forecasting models for stand-alone microgrids," Renewable Energy, Elsevier, vol. 195(C), pages 1137-1154.
  23. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
  24. Niu, Tong & Li, Jinkai & Wei, Wei & Yue, Hui, 2022. "A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting," Applied Energy, Elsevier, vol. 326(C).
  25. Manuel Jaramillo & Wilson Pavón & Lisbeth Jaramillo, 2024. "Adaptive Forecasting in Energy Consumption: A Bibliometric Analysis and Review," Data, MDPI, vol. 9(1), pages 1-23, January.
  26. 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.
  27. Joseph, Lionel P. & Deo, Ravinesh C. & Prasad, Ramendra & Salcedo-Sanz, Sancho & Raj, Nawin & Soar, Jeffrey, 2023. "Near real-time wind speed forecast model with bidirectional LSTM networks," Renewable Energy, Elsevier, vol. 204(C), pages 39-58.
  28. Hoyos-Gómez, Laura S. & Ruiz-Muñoz, Jose F. & Ruiz-Mendoza, Belizza J., 2022. "Short-term forecasting of global solar irradiance in tropical environments with incomplete data," Applied Energy, Elsevier, vol. 307(C).
  29. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms," Applied Energy, Elsevier, vol. 316(C).
  30. Elizabeth Michael, Neethu & Hasan, Shazia & Al-Durra, Ahmed & Mishra, Manohar, 2022. "Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network," Applied Energy, Elsevier, vol. 324(C).
  31. Nawin Raj, 2022. "Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning," Mathematics, MDPI, vol. 10(23), pages 1-23, November.
  32. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
  33. Shitao Wang & Mingjian Sun & Yi Shen, 2022. "Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed," Energies, MDPI, vol. 15(23), pages 1-15, November.
  34. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Interpretable deep learning models for hourly solar radiation prediction based on graph neural network and attention," Applied Energy, Elsevier, vol. 321(C).
  35. Yu, Chuanjin & Li, Yongle & Zhao, Liyang & Chen, Qian & Xun, Yuxing, 2023. "A novel time-frequency recurrent network and its advanced version for short-term wind speed predictions," Energy, Elsevier, vol. 262(PB).
  36. Siavash Asiaban & Nezmin Kayedpour & Arash E. Samani & Dimitar Bozalakov & Jeroen D. M. De Kooning & Guillaume Crevecoeur & Lieven Vandevelde, 2021. "Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System," Energies, MDPI, vol. 14(9), pages 1-41, May.
  37. Qin, Jun & Jiang, Hou & Lu, Ning & Yao, Ling & Zhou, Chenghu, 2022. "Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  38. Yin, Linfei & Cao, Xinghui & Liu, Dongduan, 2023. "Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 332(C).
  39. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George Anders, 2023. "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation," Energies, MDPI, vol. 16(10), pages 1-24, May.
  40. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
  41. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
  42. Ngoc-Lan Huynh, Anh & Deo, Ravinesh C. & Ali, Mumtaz & Abdulla, Shahab & Raj, Nawin, 2021. "Novel short-term solar radiation hybrid model: Long short-term memory network integrated with robust local mean decomposition," Applied Energy, Elsevier, vol. 298(C).
  43. Xue Zhou & Yajian Ke & Jianhui Zhu & Weiwei Cui, 2023. "Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting," Sustainability, MDPI, vol. 16(1), pages 1-26, December.
  44. Lu, Peng & Ye, Lin & Pei, Ming & Zhao, Yongning & Dai, Binhua & Li, Zhuo, 2022. "Short-term wind power forecasting based on meteorological feature extraction and optimization strategy," Renewable Energy, Elsevier, vol. 184(C), pages 642-661.
  45. 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).
  46. Narvaez, Gabriel & Giraldo, Luis Felipe & Bressan, Michael & Pantoja, Andres, 2021. "Machine learning for site-adaptation and solar radiation forecasting," Renewable Energy, Elsevier, vol. 167(C), pages 333-342.
  47. Ali, Mumtaz & Prasad, Ramendra & Xiang, Yong & Jamei, Mehdi & Yaseen, Zaher Mundher, 2023. "Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting," Renewable Energy, Elsevier, vol. 205(C), pages 731-746.
  48. Sibtain, Muhammad & Li, Xianshan & Saleem, Snoober & Ain, Qurat-ul- & Shi, Qiang & Li, Fei & Saeed, Muhammad & Majeed, Fatima & Shah, Syed Shoaib Ahmed & Saeed, Muhammad Hammad, 2022. "Multifaceted irradiance prediction by exploiting hybrid decomposition-entropy-Spatiotemporal attention based Sequence2Sequence models," Renewable Energy, Elsevier, vol. 196(C), pages 648-682.
  49. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
  50. Chinchilla, Monica & Santos-Martín, David & Carpintero-Rentería, Miguel & Lemon, Scott, 2021. "Worldwide annual optimum tilt angle model for solar collectors and photovoltaic systems in the absence of site meteorological data," Applied Energy, Elsevier, vol. 281(C).
  51. Abdel-Rahman Hedar & Majid Almaraashi & Alaa E. Abdel-Hakim & Mahmoud Abdulrahim, 2021. "Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces," Energies, MDPI, vol. 14(23), pages 1-29, November.
  52. 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.
  53. Anh Ngoc-Lan Huynh & Ravinesh C. Deo & Duc-Anh An-Vo & Mumtaz Ali & Nawin Raj & Shahab Abdulla, 2020. "Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network," Energies, MDPI, vol. 13(14), pages 1-30, July.
  54. Ajith, Meenu & Martínez-Ramón, Manel, 2021. "Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data," Applied Energy, Elsevier, vol. 294(C).
  55. Nguyen, Hoang-Phuong & Baraldi, Piero & Zio, Enrico, 2021. "Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants," Applied Energy, Elsevier, vol. 283(C).
  56. Zhao, Wei & Zhang, Haoran & Zheng, Jianqin & Dai, Yuanhao & Huang, Liqiao & Shang, Wenlong & Liang, Yongtu, 2021. "A point prediction method based automatic machine learning for day-ahead power output of multi-region photovoltaic plants," Energy, Elsevier, vol. 223(C).
  57. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).
  58. Somu, Nivethitha & Raman M R, Gauthama & Ramamritham, Krithi, 2021. "A deep learning framework for building energy consumption forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
  59. Che, Zhongyuan & Peng, Chong & Yue, Chenxiao, 2024. "Optimizing LSTM with multi-strategy improved WOA for robust prediction of high-speed machine tests data," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
  60. Rial A. Rajagukguk & Raden A. A. Ramadhan & Hyun-Jin Lee, 2020. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power," Energies, MDPI, vol. 13(24), pages 1-23, December.
  61. Wang, Jianzhou & Yu, Yue & Zeng, Bo & Lu, Haiyan, 2024. "Hybrid ultra-short-term PV power forecasting system for deterministic forecasting and uncertainty analysis," Energy, Elsevier, vol. 288(C).
  62. Jiang, Chengcheng & Zhu, Qunzhi, 2023. "Evaluating the most significant input parameters for forecasting global solar radiation of different sequences based on Informer," Applied Energy, Elsevier, vol. 348(C).
  63. Armando Castillejo-Cuberos & John Boland & Rodrigo Escobar, 2021. "Short-Term Deterministic Solar Irradiance Forecasting Considering a Heuristics-Based, Operational Approach," Energies, MDPI, vol. 14(18), pages 1-24, September.
  64. Vateanui Sansine & Pascal Ortega & Daniel Hissel & Marania Hopuare, 2022. "Solar Irradiance Probabilistic Forecasting Using Machine Learning, Metaheuristic Models and Numerical Weather Predictions," Sustainability, MDPI, vol. 14(22), pages 1-16, November.
  65. Azizi, Narjes & Yaghoubirad, Maryam & Farajollahi, Meisam & Ahmadi, Abolfzl, 2023. "Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output," Renewable Energy, Elsevier, vol. 206(C), pages 135-147.
  66. Polasek, Tomas & Čadík, Martin, 2023. "Predicting photovoltaic power production using high-uncertainty weather forecasts," Applied Energy, Elsevier, vol. 339(C).
  67. Changping Li & Xiaohui Wang & Longchen Duan & Bo Lei, 2022. "Study on a Discharge Circuit Prediction Model of High-Voltage Electro-Pulse Boring Based on Bayesian Fusion," Energies, MDPI, vol. 15(10), pages 1-12, May.
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