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Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM
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- Zhang, Yunfei & Zhou, Zhihua & Liu, Junwei & Yuan, Jianjuan, 2022. "Data augmentation for improving heating load prediction of heating substation based on TimeGAN," Energy, Elsevier, vol. 260(C).
- Liu, Jingxuan & Zang, Haixiang & Zhang, Fengchun & Cheng, Lilin & Ding, Tao & Wei, Zhinong & Sun, Guoqiang, 2023. "A hybrid meteorological data simulation framework based on time-series generative adversarial network for global daily solar radiation estimation," Renewable Energy, Elsevier, vol. 219(P1).
- 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).
- 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, 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).
- Wang, Junjie & Ye, Li & Ding, Xiaoyu & Dang, Yaoguo, 2024. "A novel seasonal grey prediction model with time-lag and interactive effects for forecasting the photovoltaic power generation," Energy, Elsevier, vol. 304(C).
- Guici Chen & Tingting Zhang & Wenyu Qu & Wenbo Wang, 2023. "Photovoltaic Power Prediction Based on VMD-BRNN-TSP," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
- Liu, Longlong & Zhou, Suyu & Jie, Qian & Du, Pei & Xu, Yan & Wang, Jianzhou, 2024. "A robust time-varying weight combined model for crude oil price forecasting," Energy, Elsevier, vol. 299(C).
- Alexandru Matei & Stefan-Alexandru Precup & Dragos Circa & Arpad Gellert & Constantin-Bala Zamfirescu, 2023. "Estimating Travel Time for Autonomous Mobile Robots through Long Short-Term Memory," Mathematics, MDPI, vol. 11(7), pages 1-19, April.
- Tian, Zhirui & Wang, Jiyang, 2023. "A wind speed prediction system based on new data preprocessing strategy and improved multi-objective optimizer," Renewable Energy, Elsevier, vol. 215(C).
- Max Olinto Moreira & Betania Mafra Kaizer & Takaaki Ohishi & Benedito Donizeti Bonatto & Antonio Carlos Zambroni de Souza & Pedro Paulo Balestrassi, 2022. "Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting," Energies, MDPI, vol. 16(1), pages 1-30, December.
- Huang, Xiaoqiao & Liu, Jun & Xu, Shaozhen & Li, Chengli & Li, Qiong & Tai, Yonghang, 2023. "A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting," Energy, Elsevier, vol. 272(C).
- Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
- Liu, Jincheng & Li, Teng, 2024. "Multi-step power forecasting for regional photovoltaic plants based on ITDE-GAT model," Energy, Elsevier, vol. 293(C).
- Meng, Anbo & Chen, Shu & Ou, Zuhong & Xiao, Jianhua & Zhang, Jianfeng & Chen, Shun & Zhang, Zheng & Liang, Ruduo & Zhang, Zhan & Xian, Zikang & Wang, Chenen & Yin, Hao & Yan, Baiping, 2022. "A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network," Energy, Elsevier, vol. 261(PA).
- Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
- Zhang, Qi & Liu, Jiangfeng & Yang, Kexin & Liu, Boyu & Wang, Ge, 2022. "Market adoption simulation of electric vehicle based on social network model considering nudge policies," Energy, Elsevier, vol. 259(C).
- Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
- Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
- 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).
- Liu, Yunfei & Liu, Yan & Cai, Hanhu & Zhang, Junran, 2023. "An innovative short-term multihorizon photovoltaic power output forecasting method based on variational mode decomposition and a capsule convolutional neural network," Applied Energy, Elsevier, vol. 343(C).
- Zafar, Muhammad Hamza & Khan, Noman Mujeeb & Houran, Mohamad Abou & Mansoor, Majad & Akhtar, Naureen & Sanfilippo, Filippo, 2024. "A novel hybrid deep learning model for accurate state of charge estimation of Li-Ion batteries for electric vehicles under high and low temperature," Energy, Elsevier, vol. 292(C).
- Zhi, Yuan & Yang, Xudong, 2023. "Scenario-based multi-objective optimization strategy for rural PV-battery systems," Applied Energy, Elsevier, vol. 345(C).
- 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).
- 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.
- Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
- Wen, Haoran & Du, Yang & Chen, Xiaoyang & Lim, Eng Gee & Wen, Huiqing & Yan, Ke, 2023. "A regional solar forecasting approach using generative adversarial networks with solar irradiance maps," Renewable Energy, Elsevier, vol. 216(C).
- Niu, Yunbo & Wang, Jianzhou & Zhang, Ziyuan & Luo, Tianrui & Liu, Jingjiang, 2024. "De-Trend First, Attend Next: A Mid-Term PV forecasting system with attention mechanism and encoder–decoder structure," Applied Energy, Elsevier, vol. 353(PB).
- Martin Geibel & Galih Bangga, 2022. "Data Reduction and Reconstruction of Wind Turbine Wake Employing Data Driven Approaches," Energies, MDPI, vol. 15(10), pages 1-40, May.
- Tan, Hongjun & Guo, Zhiling & Lin, Zhengyuan & Chen, Yuntian & Huang, Dou & Yuan, Wei & Zhang, Haoran & Yan, Jinyue, 2024. "General generative AI-based image augmentation method for robust rooftop PV segmentation," Applied Energy, Elsevier, vol. 368(C).
- Mahdi Khodayar & Jacob Regan, 2023. "Deep Neural Networks in Power Systems: A Review," Energies, MDPI, vol. 16(12), pages 1-38, June.
- Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Masahiro Furukakoi & Paras Mandal & Tomonobu Senjyu, 2023. "Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty," Energies, MDPI, vol. 16(19), pages 1-25, September.
- Luo, Haizhi & Wang, Chenglong & Li, Cangbai & Meng, Xiangzhao & Yang, Xiaohu & Tan, Qian, 2024. "Multi-scale carbon emission characterization and prediction based on land use and interpretable machine learning model: A case study of the Yangtze River Delta Region, China," Applied Energy, Elsevier, vol. 360(C).
- Zhang, Yuanting & Li, Qing & Qiu, Yu, 2024. "Real-time and annual performance evaluation of an ultra-high-temperature concentrating solar collector by developing an MCRT-CFD-ANN coupled model," Energy, Elsevier, vol. 307(C).
- Zhang, Qiongfang & Yan, Hao & Liu, Yongming, 2024. "Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).