IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v148y2018icp461-468.html
   My bibliography  Save this item

Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Guixiang Xue & Yu Pan & Tao Lin & Jiancai Song & Chengying Qi & Zhipan Wang, 2019. "District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model," Energies, MDPI, vol. 12(11), pages 1-21, June.
  2. Gao, Mingyun & Yang, Honglin & Xiao, Qinzi & Goh, Mark, 2022. "COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
  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. Yusen Wang & Wenlong Liao & Yuqing Chang, 2018. "Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting," Energies, MDPI, vol. 11(8), pages 1-14, August.
  6. Morato, Marcelo M. & Vergara-Dietrich, José & Esparcia, Eugene A. & Ocon, Joey D. & Normey-Rico, Julio E., 2021. "Assessing demand compliance and reliability in the Philippine off-grid islands with Model Predictive Control microgrid coordination," Renewable Energy, Elsevier, vol. 179(C), pages 1271-1290.
  7. 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.
  8. Mehmood, Faiza & Ghani, Muhammad Usman & Asim, Muhammad Nabeel & Shahzadi, Rehab & Mehmood, Aamir & Mahmood, Waqar, 2021. "MPF-Net: A computational multi-regional solar power forecasting framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  9. Chengqing, Yu & Guangxi, Yan & Chengming, Yu & Yu, Zhang & Xiwei, Mi, 2023. "A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks," Energy, Elsevier, vol. 263(PE).
  10. 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.
  11. Cheng, Hsu-Yung & Yu, Chih-Chang & Lin, Chih-Lung, 2021. "Day-ahead to week-ahead solar irradiance prediction using convolutional long short-term memory networks," Renewable Energy, Elsevier, vol. 179(C), pages 2300-2308.
  12. Ifaei, Pouya & Tayerani Charmchi, Amir Saman & Loy-Benitez, Jorge & Yang, Rebecca Jing & Yoo, ChangKyoo, 2022. "A data-driven analytical roadmap to a sustainable 2030 in South Korea based on optimal renewable microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  13. Majid Hosseini & Satya Katragadda & Jessica Wojtkiewicz & Raju Gottumukkala & Anthony Maida & Terrence Lynn Chambers, 2020. "Direct Normal Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 13(15), pages 1-15, July.
  14. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2022. "Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Deep Residual model for short-term multi-step solar radiation prediction," Renewable Energy, Elsevier, vol. 190(C), pages 408-424.
  15. Muhammad Naveed Akhter & Saad Mekhilef & Hazlie Mokhlis & Ziyad M. Almohaimeed & Munir Azam Muhammad & Anis Salwa Mohd Khairuddin & Rizwan Akram & Muhammad Majid Hussain, 2022. "An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants," Energies, MDPI, vol. 15(6), pages 1-21, March.
  16. Lima, Marcello Anderson F.B. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M. & Braga, Arthur P.S., 2020. "Improving solar forecasting using Deep Learning and Portfolio Theory integration," Energy, Elsevier, vol. 195(C).
  17. 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).
  18. 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).
  19. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-step solar irradiation prediction based on weather forecast and generative deep learning model," Renewable Energy, Elsevier, vol. 188(C), pages 637-650.
  20. Nourani, Vahid & Sharghi, Elnaz & Behfar, Nazanin & Zhang, Yongqiang, 2022. "Multi-step-ahead solar irradiance modeling employing multi-frequency deep learning models and climatic data," Applied Energy, Elsevier, vol. 315(C).
  21. Hao Zhen & Dongxiao Niu & Min Yu & Keke Wang & Yi Liang & Xiaomin Xu, 2020. "A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
  22. Maria Krechowicz & Adam Krechowicz & Lech Lichołai & Artur Pawelec & Jerzy Zbigniew Piotrowski & Anna Stępień, 2022. "Reduction of the Risk of Inaccurate Prediction of Electricity Generation from PV Farms Using Machine Learning," Energies, MDPI, vol. 15(11), pages 1-21, May.
  23. Chen, Yunxiao & Bai, Mingliang & Zhang, Yilan & Liu, Jinfu & Yu, Daren, 2023. "Proactively selection of input variables based on information gain factors for deep learning models in short-term solar irradiance forecasting," Energy, Elsevier, vol. 284(C).
  24. Yukta Mehta & Rui Xu & Benjamin Lim & Jane Wu & Jerry Gao, 2023. "A Review for Green Energy Machine Learning and AI Services," Energies, MDPI, vol. 16(15), pages 1-30, July.
  25. Huang, Songtao & Zhou, Qingguo & Shen, Jun & Zhou, Heng & Yong, Binbin, 2024. "Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting," Energy, Elsevier, vol. 290(C).
  26. 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).
  27. Fei Qian & Li Chen & Jun Li & Chao Ding & Xianfu Chen & Jian Wang, 2019. "Direct Prediction of the Toxic Gas Diffusion Rule in a Real Environment Based on LSTM," IJERPH, MDPI, vol. 16(12), pages 1-14, June.
  28. Wang, Zhenyu & Zhang, Yunpeng & Li, Guorong & Zhang, Jinlong & Zhou, Hai & Wu, Ji, 2024. "A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model," Renewable Energy, Elsevier, vol. 226(C).
  29. 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).
  30. Hui Huang & Qiliang Zhu & Xueling Zhu & Jinhua Zhang, 2023. "An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation," Energies, MDPI, vol. 16(4), pages 1-20, February.
  31. 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).
  32. 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.
  33. Hai Tao & Isa Ebtehaj & Hossein Bonakdari & Salim Heddam & Cyril Voyant & Nadhir Al-Ansari & Ravinesh Deo & Zaher Mundher Yaseen, 2019. "Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme," Energies, MDPI, vol. 12(7), pages 1-24, April.
  34. Chen, Yibo & Zhang, Fengyi & Berardi, Umberto, 2020. "Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms," Energy, Elsevier, vol. 211(C).
  35. Byung-ki Jeon & Eui-Jong Kim, 2020. "Next-Day Prediction of Hourly Solar Irradiance Using Local Weather Forecasts and LSTM Trained with Non-Local Data," Energies, MDPI, vol. 13(20), pages 1-16, October.
  36. Jesús Polo & Nuria Martín-Chivelet & Miguel Alonso-Abella & Carlos Sanz-Saiz & José Cuenca & Marina de la Cruz, 2023. "Exploring the PV Power Forecasting at Building Façades Using Gradient Boosting Methods," Energies, MDPI, vol. 16(3), pages 1-12, February.
  37. Sourav Malakar & Saptarsi Goswami & Bhaswati Ganguli & Amlan Chakrabarti & Sugata Sen Roy & K. Boopathi & A. G. Rangaraj, 2022. "Deep-Learning-Based Adaptive Model for Solar Forecasting Using Clustering," Energies, MDPI, vol. 15(10), pages 1-16, May.
  38. 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.
  39. 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).
  40. Seok-Jun Bu & Sung-Bae Cho, 2020. "Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-16, September.
  41. 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.
  42. Yi Liu & Jun He & Yu Wang & Zong Liu & Lixun He & Yanyang Wang, 2023. "Short-Term Wind Power Prediction Based on CEEMDAN-SE and Bidirectional LSTM Neural Network with Markov Chain," Energies, MDPI, vol. 16(14), pages 1-25, July.
  43. Elsa Chaerun Nisa & Yean-Der Kuan, 2021. "Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
  44. Dewangan, Chaman Lal & Singh, S.N. & Chakrabarti, S., 2020. "Combining forecasts of day-ahead solar power," Energy, Elsevier, vol. 202(C).
  45. 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).
  46. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
  47. Tukymbekov, Didar & Saymbetov, Ahmet & Nurgaliyev, Madiyar & Kuttybay, Nurzhigit & Dosymbetova, Gulbakhar & Svanbayev, Yeldos, 2021. "Intelligent autonomous street lighting system based on weather forecast using LSTM," Energy, Elsevier, vol. 231(C).
  48. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
  49. Gupta, Priya & Singh, Rhythm, 2023. "Combining simple and less time complex ML models with multivariate empirical mode decomposition to obtain accurate GHI forecast," Energy, Elsevier, vol. 263(PC).
  50. 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.
  51. Hongze Li & Hongyu Liu & Hongyan Ji & Shiying Zhang & Pengfei Li, 2020. "Ultra-Short-Term Load Demand Forecast Model Framework Based on Deep Learning," Energies, MDPI, vol. 13(18), pages 1-16, September.
  52. 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).
  53. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).
  54. Kelachukwu J. Iheanetu, 2022. "Solar Photovoltaic Power Forecasting: A Review," Sustainability, MDPI, vol. 14(24), pages 1-31, December.
  55. 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.
  56. 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).
  57. Neshat, Mehdi & Nezhad, Meysam Majidi & Mirjalili, Seyedali & Garcia, Davide Astiaso & Dahlquist, Erik & Gandomi, Amir H., 2023. "Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution strategy," Energy, Elsevier, vol. 278(C).
  58. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  59. Bisoi, Ranjeeta & Dash, Deepak Ranjan & Dash, P.K. & Tripathy, Lokanath, 2022. "An efficient robust optimized functional link broad learning system for solar irradiance prediction," Applied Energy, Elsevier, vol. 319(C).
  60. Zhengwei Huang & Jin Huang & Jintao Min, 2022. "SSA-LSTM: Short-Term Photovoltaic Power Prediction Based on Feature Matching," Energies, MDPI, vol. 15(20), pages 1-16, October.
  61. Ying Wang & Bo Feng & Qing-Song Hua & Li Sun, 2021. "Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
  62. Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.
  63. 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).
  64. Zhang, Chao & Ma, Yunfeng & Mi, Zengqiang & Yang, Fan & Zhang, Long, 2024. "A rolling-horizon cleaning recommendation system for dust removal of industrial PV panels," Applied Energy, Elsevier, vol. 353(PB).
  65. Lu, Yakai & Tian, Zhe & Zhang, Qiang & Zhou, Ruoyu & Chu, Chengshan, 2021. "Data augmentation strategy for short-term heating load prediction model of residential building," Energy, Elsevier, vol. 235(C).
  66. 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).
  67. 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).
  68. 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).
  69. Prevedello, Giulio & Werth, Annette, 2021. "The benefits of sharing in off-grid microgrids: A case study in the Philippines," Applied Energy, Elsevier, vol. 303(C).
  70. Zhang, Jinhua & Yan, Jie & Infield, David & Liu, Yongqian & Lien, Fue-sang, 2019. "Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model," Applied Energy, Elsevier, vol. 241(C), pages 229-244.
  71. Li, Chaoshun & Tang, Geng & Xue, Xiaoming & Chen, Xinbiao & Wang, Ruoheng & Zhang, Chu, 2020. "The short-term interval prediction of wind power using the deep learning model with gradient descend optimization," Renewable Energy, Elsevier, vol. 155(C), pages 197-211.
  72. Zamani Gargari, Milad & Ghaffarpour, Reza, 2020. "Reliability evaluation of multi-carrier energy system with different level of demands under various weather situation," Energy, Elsevier, vol. 196(C).
  73. Perera, Maneesha & De Hoog, Julian & Bandara, Kasun & Senanayake, Damith & Halgamuge, Saman, 2024. "Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data," Applied Energy, Elsevier, vol. 361(C).
  74. 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).
  75. Mahdi Khodayar & Jacob Regan, 2023. "Deep Neural Networks in Power Systems: A Review," Energies, MDPI, vol. 16(12), pages 1-38, June.
  76. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
  77. Kumari, Pratima & Toshniwal, Durga, 2021. "Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting," Applied Energy, Elsevier, vol. 295(C).
  78. Shen, Hongzheng & Wang, Yue & Jiang, Kongtao & Li, Shilei & Huang, Donghua & Wu, Jiujiang & Wang, Yongqiang & Wang, Yangren & Ma, Xiaoyi, 2022. "Simulation modeling for effective management of irrigation water for winter wheat," Agricultural Water Management, Elsevier, vol. 269(C).
  79. 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).
  80. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
  81. Ze Wu & Feifan Pan & Dandan Li & Hao He & Tiancheng Zhang & Shuyun Yang, 2022. "Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
  82. Ladislav Zjavka, 2023. "Solar and Wind Quantity 24 h—Series Prediction Using PDE-Modular Models Gradually Developed according to Spatial Pattern Similarity," Energies, MDPI, vol. 16(3), pages 1-16, January.
  83. Cong Cao & Suzana Dragićević & Songnian Li, 2019. "Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models," Sustainability, MDPI, vol. 11(19), pages 1-18, September.
  84. Ajith, Meenu & Martínez-Ramón, Manel, 2023. "Deep learning algorithms for very short term solar irradiance forecasting: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
  85. 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.
  86. Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning," Energies, MDPI, vol. 12(17), pages 1-18, August.
  87. Su, Huai & Zio, Enrico & Zhang, Jinjun & Xu, Mingjing & Li, Xueyi & Zhang, Zongjie, 2019. "A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model," Energy, Elsevier, vol. 178(C), pages 585-597.
  88. 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).
  89. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
  90. Zhang, Lihong & Wang, Jun & Wang, Bin, 2020. "Energy market prediction with novel long short-term memory network: Case study of energy futures index volatility," Energy, Elsevier, vol. 211(C).
  91. Xin Jing & Jungang Luo & Jingmin Wang & Ganggang Zuo & Na Wei, 2022. "A Multi-imputation Method to Deal With Hydro-Meteorological Missing Values by Integrating Chain Equations and Random Forest," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1159-1173, March.
  92. 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.
  93. Jessica Wojtkiewicz & Matin Hosseini & Raju Gottumukkala & Terrence Lynn Chambers, 2019. "Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 12(21), pages 1-13, October.
  94. 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.
  95. Trigo-González, Mauricio & Cortés-Carmona, Marcelo & Marzo, Aitor & Alonso-Montesinos, Joaquín & Martínez-Durbán, Mercedes & López, Gabriel & Portillo, Carlos & Batlles, Francisco Javier, 2023. "Photovoltaic power electricity generation nowcasting combining sky camera images and learning supervised algorithms in the Southern Spain," Renewable Energy, Elsevier, vol. 206(C), pages 251-262.
  96. Shubham Gupta & Amit Kumar Singh & Sachin Mishra & Pradeep Vishnuram & Nagaraju Dharavat & Narayanamoorthi Rajamanickam & Ch. Naga Sai Kalyan & Kareem M. AboRas & Naveen Kumar Sharma & Mohit Bajaj, 2023. "Estimation of Solar Radiation with Consideration of Terrestrial Losses at a Selected Location—A Review," Sustainability, MDPI, vol. 15(13), pages 1-29, June.
  97. 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).
  98. 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).
  99. Eugenio Borghini & Cinzia Giannetti & James Flynn & Grazia Todeschini, 2021. "Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation," Energies, MDPI, vol. 14(12), pages 1-22, June.
  100. Christil Pasion & Torrey Wagner & Clay Koschnick & Steven Schuldt & Jada Williams & Kevin Hallinan, 2020. "Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data," Energies, MDPI, vol. 13(10), pages 1-14, May.
  101. 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.
  102. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
  103. 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).
  104. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
  105. 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.
  106. Zheng, Jianqin & Zhang, Haoran & Dai, Yuanhao & Wang, Bohong & Zheng, Taicheng & Liao, Qi & Liang, Yongtu & Zhang, Fengwei & Song, Xuan, 2020. "Time series prediction for output of multi-region solar power plants," Applied Energy, Elsevier, vol. 257(C).
  107. 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.
  108. 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).
  109. Luo, Xing & Zhang, Dongxiao & Zhu, Xu, 2021. "Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge," Energy, Elsevier, vol. 225(C).
  110. Huber, Jakob & Stuckenschmidt, Heiner, 2021. "Intraday shelf replenishment decision support for perishable goods," International Journal of Production Economics, Elsevier, vol. 231(C).
  111. 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.
  112. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
  113. Kaijian He & Qian Yang & Lei Ji & Jingcheng Pan & Yingchao Zou, 2023. "Financial Time Series Forecasting with the Deep Learning Ensemble Model," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
  114. Liu, Hui & Yu, Chengqing & Wu, Haiping & Duan, Zhu & Yan, Guangxi, 2020. "A new hybrid ensemble deep reinforcement learning model for wind speed short term forecasting," Energy, Elsevier, vol. 202(C).
  115. Salimian Rizi, Behzad & Pavlak, Gregory & Cushing, Vincent & Heidarinejad, Mohammad, 2023. "Predicting uncertainty of a chiller plant power consumption using quantile random forest: A commercial building case study," Energy, Elsevier, vol. 283(C).
  116. N. Yogambal Jayalakshmi & R. Shankar & Umashankar Subramaniam & I. Baranilingesan & Alagar Karthick & Balasubramaniam Stalin & Robbi Rahim & Aritra Ghosh, 2021. "Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting," Energies, MDPI, vol. 14(9), pages 1-23, April.
  117. Edna S. Solano & Carolina M. Affonso, 2023. "Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
  118. Teng, Sin Yong & Máša, Vítězslav & Touš, Michal & Vondra, Marek & Lam, Hon Loong & Stehlík, Petr, 2022. "Waste-to-energy forecasting and real-time optimization: An anomaly-aware approach," Renewable Energy, Elsevier, vol. 181(C), pages 142-155.
  119. Haider, Syed Altan & Sajid, Muhammad & Sajid, Hassan & Uddin, Emad & Ayaz, Yasar, 2022. "Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad," Renewable Energy, Elsevier, vol. 198(C), pages 51-60.
  120. Dengyong Zhang & Haixin Tong & Feng Li & Lingyun Xiang & Xiangling Ding, 2020. "An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model," Energies, MDPI, vol. 13(18), pages 1-14, September.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.