IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v251y2019ic30.html
   My bibliography  Save this item

A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network

Citations

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


Cited by:

  1. Akhlaque Ahmad Khan & Ahmad Faiz Minai & Rupendra Kumar Pachauri & Hasmat Malik, 2022. "Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review," Energies, MDPI, vol. 15(17), pages 1-29, August.
  2. Li, Wenqiang & Gong, Guangcai & Fan, Houhua & Peng, Pei & Chun, Liang & Fang, Xi, 2021. "A clustering-based approach for “cross-scale” load prediction on building level in HVAC systems," Applied Energy, Elsevier, vol. 282(PB).
  3. Mayer, Martin János, 2022. "Benefits of physical and machine learning hybridization for photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
  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. Cheng, Lilin & Zang, Haixiang & Wei, Zhinong & Zhang, Fengchun & Sun, Guoqiang, 2022. "Evaluation of opaque deep-learning solar power forecast models towards power-grid applications," Renewable Energy, Elsevier, vol. 198(C), pages 960-972.
  6. Lyu, Chenghao & Zhang, Yuchen & Bai, Yilin & Yang, Kun & Song, Zhengxiang & Ma, Yuhang & Meng, Jinhao, 2024. "Inner-outer layer co-optimization of sizing and energy management for renewable energy microgrid with storage," Applied Energy, Elsevier, vol. 363(C).
  7. 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.
  8. Mingliang Bai & Jinfu Liu & Yujia Ma & Xinyu Zhao & Zhenhua Long & Daren Yu, 2020. "Long Short-Term Memory Network-Based Normal Pattern Group for Fault Detection of Three-Shaft Marine Gas Turbine," Energies, MDPI, vol. 14(1), pages 1-22, December.
  9. 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.
  10. 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.
  11. Leidy Gutiérrez & Julian Patiño & Eduardo Duque-Grisales, 2021. "A Comparison of the Performance of Supervised Learning Algorithms for Solar Power Prediction," Energies, MDPI, vol. 14(15), pages 1-16, July.
  12. Li, Xingshuo & Liu, Jinfu & Bai, Mingliang & Li, Jiajia & Li, Xianling & Yan, Peigang & Yu, Daren, 2021. "An LSTM based method for stage performance degradation early warning with consideration of time-series information," Energy, Elsevier, vol. 226(C).
  13. Ren, Xiaoying & Zhang, Fei & Zhu, Honglu & Liu, Yongqian, 2022. "Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting," Applied Energy, Elsevier, vol. 323(C).
  14. 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).
  15. Markovics, Dávid & Mayer, Martin János, 2022. "Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
  16. 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).
  17. 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).
  18. 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.
  19. 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.
  20. Li, Naiqing & Li, Longhao & Zhang, Fan & Jiao, Ticao & Wang, Shuang & Liu, Xuefeng & Wu, Xinghua, 2023. "Research on short-term photovoltaic power prediction based on multi-scale similar days and ESN-KELM dual core prediction model," Energy, Elsevier, vol. 277(C).
  21. Xilong Lin & Yisen Niu & Zixuan Yan & Lianglin Zou & Ping Tang & Jifeng Song, 2024. "Hybrid Photovoltaic Output Forecasting Model with Temporal Convolutional Network Using Maximal Information Coefficient and White Shark Optimizer," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
  22. Mohamed Trabelsi & Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Shady S. Refaat & Tingwen Huang & Fakhreddine S. Oueslati, 2022. "An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting," Energies, MDPI, vol. 15(23), pages 1-14, November.
  23. 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).
  24. 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.
  25. Wang, Haoxuan & Chen, Huaian & Wang, Ben & Jin, Yi & Li, Guiqiang & Kan, Yan, 2022. "High-efficiency low-power microdefect detection in photovoltaic cells via a field programmable gate array-accelerated dual-flow network," Applied Energy, Elsevier, vol. 318(C).
  26. 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).
  27. Zhi, Yuan & Yang, Xudong, 2023. "Scenario-based multi-objective optimization strategy for rural PV-battery systems," Applied Energy, Elsevier, vol. 345(C).
  28. Gulay, Emrah & Sen, Mustafa & Akgun, Omer Burak, 2024. "Forecasting electricity production from various energy sources in Türkiye: A predictive analysis of time series, deep learning, and hybrid models," Energy, Elsevier, vol. 286(C).
  29. 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).
  30. Khan, Zulfiqar Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2023. "Dual stream network with attention mechanism for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 338(C).
  31. 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.
  32. 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).
  33. Asmita Ajay Rathod & Balaji Subramanian, 2022. "Scrutiny of Hybrid Renewable Energy Systems for Control, Power Management, Optimization and Sizing: Challenges and Future Possibilities," Sustainability, MDPI, vol. 14(24), pages 1-35, December.
  34. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
  35. Bellido-Jiménez, Juan Antonio & Estévez Gualda, Javier & García-Marín, Amanda Penélope, 2021. "Assessing new intra-daily temperature-based machine learning models to outperform solar radiation predictions in different conditions," Applied Energy, Elsevier, vol. 298(C).
  36. Verdone, Alessio & Scardapane, Simone & Panella, Massimo, 2024. "Explainable Spatio-Temporal Graph Neural Networks for multi-site photovoltaic energy production," Applied Energy, Elsevier, vol. 353(PB).
  37. 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.
  38. 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.
  39. Fachrizal Aksan & Vishnu Suresh & Przemysław Janik & Tomasz Sikorski, 2023. "Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models," Energies, MDPI, vol. 16(14), pages 1-24, July.
  40. Kaiyan Wang & Haodong Du & Rong Jia & Hongtao Jia, 2022. "Performance Comparison of Bayesian Deep Learning Model and Traditional Bayesian Neural Network in Short-Term PV Interval Prediction," Sustainability, MDPI, vol. 14(19), pages 1-27, October.
  41. 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).
  42. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
  43. Shi, Yu & Song, Xianzhi & Song, Guofeng, 2021. "Productivity prediction of a multilateral-well geothermal system based on a long short-term memory and multi-layer perceptron combinational neural network," Applied Energy, Elsevier, vol. 282(PA).
  44. 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.
  45. 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).
  46. 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).
  47. Fatma Yaprakdal & M. Berkay Yılmaz & Mustafa Baysal & Amjad Anvari-Moghaddam, 2020. "A Deep Neural Network-Assisted Approach to Enhance Short-Term Optimal Operational Scheduling of a Microgrid," Sustainability, MDPI, vol. 12(4), pages 1-27, February.
  48. Yu, Min & Niu, Dongxiao & Wang, Keke & Du, Ruoyun & Yu, Xiaoyu & Sun, Lijie & Wang, Feiran, 2023. "Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification," Energy, Elsevier, vol. 275(C).
  49. Jinfeng Wang & Wenshan Hu & Lingfeng Xuan & Feiwu He & Chaojie Zhong & Guowei Guo, 2024. "TransPVP: A Transformer-Based Method for Ultra-Short-Term Photovoltaic Power Forecasting," Energies, MDPI, vol. 17(17), pages 1-19, September.
  50. Bo, Yaolong & Xia, Yanghong & Wei, Wei & Li, Zichen & Zhao, Bo & Lv, Zeyan, 2023. "Hyperfine optimal dispatch for integrated energy microgrid considering uncertainty," Applied Energy, Elsevier, vol. 334(C).
  51. 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).
  52. 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).
  53. 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).
  54. Wang, Chong & Ju, Ping & Wu, Feng & Pan, Xueping & Wang, Zhaoyu, 2022. "A systematic review on power system resilience from the perspective of generation, network, and load," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  55. 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.
  56. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
  57. Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
  58. 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).
  59. Ziran Yuan & Pengli Zhang & Bo Ming & Xiaobo Zheng & Lu Tian, 2023. "Joint Forecasting Method of Wind and Solar Outputs Considering Temporal and Spatial Correlation," Sustainability, MDPI, vol. 15(19), pages 1-16, October.
  60. 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).
  61. Graça Gomes, J. & Xu, H.J. & Yang, Q. & Zhao, C.Y., 2021. "An optimization study on a typical renewable microgrid energy system with energy storage," Energy, Elsevier, vol. 234(C).
  62. Kandpal, Bakul & Backe, Stian & Crespo del Granado, Pedro, 2024. "Power purchase agreements for plus energy neighbourhoods: Financial risk mitigation through predictive modelling and bargaining theory," Applied Energy, Elsevier, vol. 358(C).
  63. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
  64. Du, Bin & Lund, Peter D. & Wang, Jun, 2021. "Combining CFD and artificial neural network techniques to predict the thermal performance of all-glass straight evacuated tube solar collector," Energy, Elsevier, vol. 220(C).
  65. Xiaorui Shao & Chang-Soo Kim & Palash Sontakke, 2020. "Accurate Deep Model for Electricity Consumption Forecasting Using Multi-Channel and Multi-Scale Feature Fusion CNN–LSTM," Energies, MDPI, vol. 13(8), pages 1-22, April.
  66. du Plessis, A.A. & Strauss, J.M. & Rix, A.J., 2021. "Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour," Applied Energy, Elsevier, vol. 285(C).
  67. Grzegorz Drałus & Damian Mazur & Jacek Kusznier & Jakub Drałus, 2023. "Application of Artificial Intelligence Algorithms in Multilayer Perceptron and Elman Networks to Predict Photovoltaic Power Plant Generation," Energies, MDPI, vol. 16(18), pages 1-23, September.
  68. 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.
  69. Ashish Sedai & Rabin Dhakal & Shishir Gautam & Anibesh Dhamala & Argenis Bilbao & Qin Wang & Adam Wigington & Suhas Pol, 2023. "Performance Analysis of Statistical, Machine Learning and Deep Learning Models in Long-Term Forecasting of Solar Power Production," Forecasting, MDPI, vol. 5(1), pages 1-29, February.
  70. Cai Tao & Junjie Lu & Jianxun Lang & Xiaosheng Peng & Kai Cheng & Shanxu Duan, 2021. "Short-Term Forecasting of Photovoltaic Power Generation Based on Feature Selection and Bias Compensation–LSTM Network," Energies, MDPI, vol. 14(11), pages 1-16, May.
  71. 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).
  72. Yuhao Zhang & Ting Li & Tianyi Ma & Dongsheng Yang & Xiaolong Sun, 2024. "Short-Term Photovoltaic Power Prediction Based on Extreme Learning Machine with Improved Dung Beetle Optimization Algorithm," Energies, MDPI, vol. 17(4), pages 1-24, February.
  73. Liu, Jiarui & Fu, Yuchen, 2023. "Decomposition spectral graph convolutional network based on multi-channel adaptive adjacency matrix for renewable energy prediction," Energy, Elsevier, vol. 284(C).
  74. Yunzhu Gao & Jun Wang & Lin Guo & Hong Peng, 2024. "Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems," Sustainability, MDPI, vol. 16(4), pages 1-18, February.
  75. Soleimanzade, Mohammad Amin & Sadrzadeh, Mohtada, 2021. "Deep learning-based energy management of a hybrid photovoltaic-reverse osmosis-pressure retarded osmosis system," Applied Energy, Elsevier, vol. 293(C).
  76. Fachrizal Aksan & Yang Li & Vishnu Suresh & Przemysław Janik, 2023. "Multistep Forecasting of Power Flow Based on LSTM Autoencoder: A Study Case in Regional Grid Cluster Proposal," Energies, MDPI, vol. 16(13), pages 1-20, June.
  77. Xiaoying Ren & Fei Zhang & Yongrui Sun & Yongqian Liu, 2024. "A Novel Dual-Channel Temporal Convolutional Network for Photovoltaic Power Forecasting," Energies, MDPI, vol. 17(3), pages 1-19, February.
  78. Khushwant Rai & Farnam Hojatpanah & Firouz Badrkhani Ajaei & Katarina Grolinger, 2021. "Deep Learning for High-Impedance Fault Detection: Convolutional Autoencoders," Energies, MDPI, vol. 14(12), pages 1-25, June.
  79. 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.
  80. 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.
  81. Dong-Dong Yuan & Ming Li & Heng-Yi Li & Cheng-Jian Lin & Bing-Xiang Ji, 2022. "Wind Power Prediction Method: Support Vector Regression Optimized by Improved Jellyfish Search Algorithm," Energies, MDPI, vol. 15(17), pages 1-19, September.
  82. 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).
  83. Mashlakov, Aleksei & Kuronen, Toni & Lensu, Lasse & Kaarna, Arto & Honkapuro, Samuli, 2021. "Assessing the performance of deep learning models for multivariate probabilistic energy forecasting," Applied Energy, Elsevier, vol. 285(C).
  84. Nonthawat Khortsriwong & Promphak Boonraksa & Terapong Boonraksa & Thipwan Fangsuwannarak & Asada Boonsrirat & Watcharakorn Pinthurat & Boonruang Marungsri, 2023. "Performance of Deep Learning Techniques for Forecasting PV Power Generation: A Case Study on a 1.5 MWp Floating PV Power Plant," Energies, MDPI, vol. 16(5), pages 1-21, February.
  85. Dash, Deepak Ranjan & Dash, P.K. & Bisoi, Ranjeeta, 2021. "Short term solar power forecasting using hybrid minimum variance expanded RVFLN and Sine-Cosine Levy Flight PSO algorithm," Renewable Energy, Elsevier, vol. 174(C), pages 513-537.
  86. Yin, Linfei & Xie, Jiaxing, 2021. "Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems," Applied Energy, Elsevier, vol. 283(C).
  87. 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.
  88. 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).
  89. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
  90. 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).
  91. Ahn, Hyeunguk, 2024. "A framework for developing data-driven correction factors for solar PV systems," Energy, Elsevier, vol. 290(C).
  92. 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).
  93. Ahmed Faris Amiri & Aissa Chouder & Houcine Oudira & Santiago Silvestre & Sofiane Kichou, 2024. "Improving Photovoltaic Power Prediction: Insights through Computational Modeling and Feature Selection," Energies, MDPI, vol. 17(13), pages 1-23, June.
  94. 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.
  95. Medine Colak & Mehmet Yesilbudak & Ramazan Bayindir, 2020. "Daily Photovoltaic Power Prediction Enhanced by Hybrid GWO-MLP, ALO-MLP and WOA-MLP Models Using Meteorological Information," Energies, MDPI, vol. 13(4), pages 1-19, February.
  96. Xiaoying Ren & Fei Zhang & Junshuai Yan & Yongqian Liu, 2024. "A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting," Sustainability, MDPI, vol. 16(7), pages 1-21, March.
  97. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).
  98. Tang, Rui & Dore, Jonathon & Ma, Jin & Leong, Philip H.W., 2021. "Interpolating high granularity solar generation and load consumption data using super resolution generative adversarial network," Applied Energy, Elsevier, vol. 299(C).
  99. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.