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Forecasting monthly gas field production based on the CNN-LSTM model
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- Wang, Kun & Peng, Zhenghong & Cai, Meng & Wu, Hao & Liu, Lingbo & Sun, Zhihao, 2024. "Coupling graph neural networks and travel mode choice for human mobility prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 646(C).
- Shi, Changfeng & Zhi, Jiaqi & Yao, Xiao & Zhang, Hong & Yu, Yue & Zeng, Qingshun & Li, Luji & Zhang, Yuxi, 2023. "How can China achieve the 2030 carbon peak goal—a crossover analysis based on low-carbon economics and deep learning," Energy, Elsevier, vol. 269(C).
- Fargalla, Mandella Ali M. & Yan, Wei & Deng, Jingen & Wu, Tao & Kiyingi, Wyclif & Li, Guangcong & Zhang, Wei, 2024. "TimeNet: Time2Vec attention-based CNN-BiGRU neural network for predicting production in shale and sandstone gas reservoirs," Energy, Elsevier, vol. 290(C).
- Fuquan Song & Heying Ding & Yongzheng Wang & Shiming Zhang & Jinbiao Yu, 2023. "A Well Production Prediction Method of Tight Reservoirs Based on a Hybrid Neural Network," Energies, MDPI, vol. 16(6), pages 1-22, March.
- Wang, Jun & Cao, Junxing, 2024. "Reservoir properties inversion using attention-based parallel hybrid network integrating feature selection and transfer learning," Energy, Elsevier, vol. 304(C).
- Aygun, Hakan & Dursun, Omer Osman & Toraman, Suat, 2023. "Machine learning based approach for forecasting emission parameters of mixed flow turbofan engine at high power modes," Energy, Elsevier, vol. 271(C).
- Li, Baozhu & Lv, Xiaotian & Chen, Jiaxin, 2024. "Demand and supply gap analysis of Chinese new energy vehicle charging infrastructure: Based on CNN-LSTM prediction model," Renewable Energy, Elsevier, vol. 220(C).
- Xianlin Ma & Long Zhang & Jie Zhan & Shilong Chang, 2024. "Application of Dual-Stage Attention Temporal Convolutional Networks in Gas Well Production Prediction," Mathematics, MDPI, vol. 12(24), pages 1-16, December.
- Fang, Yu & Jia, Chunhong & Wang, Xin & Min, Fan, 2024. "A fusion gas load prediction model with three-way residual error amendment," Energy, Elsevier, vol. 294(C).
- Banglong Pan & Hanming Yu & Hongwei Cheng & Shuhua Du & Shutong Cai & Minle Zhao & Juan Du & Fazhi Xie, 2023. "A CNN–LSTM Machine-Learning Method for Estimating Particulate Organic Carbon from Remote Sensing in Lakes," Sustainability, MDPI, vol. 15(17), pages 1-15, August.
- Zhou, Wei & Li, Xiangchengzhen & Qi, ZhongLi & Zhao, HaiHang & Yi, Jun, 2024. "A shale gas production prediction model based on masked convolutional neural network," Applied Energy, Elsevier, vol. 353(PA).
- Meng, Anbo & Xie, Zhifeng & Luo, Jianqiang & Zeng, Ying & Xu, Xuancong & Li, Yidian & Wu, Zhenbo & Zhang, Zhan & Zhu, Jianbin & Xian, Zikang & Li, Chen & Yan, Baiping & Yin, Hao, 2023. "An adaptive variational mode decomposition for wind power prediction using convolutional block attention deep learning network," Energy, Elsevier, vol. 282(C).
- Feng, Zhanyu & Zhang, Jian & Jiang, Han & Yao, Xuejian & Qian, Yu & Zhang, Haiyan, 2024. "Energy consumption prediction strategy for electric vehicle based on LSTM-transformer framework," Energy, Elsevier, vol. 302(C).
- Mi, Hanning & Chen, Sijie & Li, Qingxin & Shi, Ming & Hou, Shuoming & Zheng, Linfeng & Xu, Chengke & Yan, Zheng & Li, Canbing, 2024. "Strategic bidding by predicting locational marginal price with aggregated supply curve," Energy, Elsevier, vol. 304(C).
- Li, Chunxiao & Cui, Can & Li, Ming, 2023. "A proactive 2-stage indoor CO2-based demand-controlled ventilation method considering control performance and energy efficiency," Applied Energy, Elsevier, vol. 329(C).
- Zhang, Yagang & Pan, Zhiya & Wang, Hui & Wang, Jingchao & Zhao, Zheng & Wang, Fei, 2023. "Achieving wind power and photovoltaic power prediction: An intelligent prediction system based on a deep learning approach," Energy, Elsevier, vol. 283(C).
- 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).
- Jiang, Wei & Wang, Xin & Zhang, Shu, 2023. "Integrating multi-modal data into AFSA-LSTM model for real-time oil production prediction," Energy, Elsevier, vol. 279(C).
- Aniket Vatsa & Ananda Shankar Hati & Vadim Bolshev & Alexander Vinogradov & Vladimir Panchenko & Prasun Chakrabarti, 2023. "Deep Learning-Based Transformer Moisture Diagnostics Using Long Short-Term Memory Networks," Energies, MDPI, vol. 16(5), pages 1-14, March.
- Zhang, Yunfei & Zhou, Zhihua & Du, Yahui & Shen, Jun & Li, Zhenxing & Yuan, Jianjuan, 2023. "A data transfer method based on one dimensional convolutional neural network for cross-building load prediction," Energy, Elsevier, vol. 277(C).
- Dong, Juan & Xing, Liwen & Cui, Ningbo & Zhao, Lu & Guo, Li & Wang, Zhihui & Du, Taisheng & Tan, Mingdong & Gong, Daozhi, 2024. "Estimating reference crop evapotranspiration using improved convolutional bidirectional long short-term memory network by multi-head attention mechanism in the four climatic zones of China," Agricultural Water Management, Elsevier, vol. 292(C).
- Yang, Jiuqiang & Lin, Niantian & Zhang, Kai & Fu, Chao & Zhang, Chong, 2024. "Transfer learning-based hybrid deep learning method for gas-bearing distribution prediction with insufficient training samples and uncertainty analysis," Energy, Elsevier, vol. 299(C).
- Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(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).
- Xiangming Kong & Yuetian Liu & Liang Xue & Guanlin Li & Dongdong Zhu, 2023. "A Hybrid Oil Production Prediction Model Based on Artificial Intelligence Technology," Energies, MDPI, vol. 16(3), pages 1-16, January.
- Daihong Gu & Rongchen Zheng & Peng Cheng & Shuaiqi Zhou & Gongjie Yan & Haitao Liu & Kexin Yang & Jianguo Wang & Yuan Zhu & Mingwei Liao, 2024. "Single Well Production Prediction Model of Gas Reservoir Based on CNN-BILSTM-AM," Energies, MDPI, vol. 17(22), pages 1-18, November.
- Juhyun Kim & Sunlee Han & Daehee Kim & Youngsoo Lee, 2024. "Gas Pipeline Leak Detection by Integrating Dynamic Modeling and Machine Learning Under the Transient State," Energies, MDPI, vol. 17(21), pages 1-23, November.
- Li, Daolun & Zhou, Xia & Xu, Yanmei & Wan, Yujin & Zha, Wenshu, 2023. "Deep learning-based analysis of the main controlling factors of different gas-fields recovery rate," Energy, Elsevier, vol. 285(C).
- Dong, Fangying & Yin, Huiyong & Cheng, Wenju & Zhang, Chao & Zhang, Danyang & Ding, Haixiao & Lu, Chang & Wang, Yin, 2024. "Quantitative prediction model and prewarning system of water yield capacity (WYC) from coal seam roof based on deep learning and joint advanced detection," Energy, Elsevier, vol. 290(C).
- Mohammad Ehtearm & Hossein Ghayoumi Zadeh & Akram Seifi & Ali Fayazi & Majid Dehghani, 2023. "Predicting Hydropower Production Using Deep Learning CNN-ANN Hybridized with Gaussian Process Regression and Salp Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3671-3697, July.
- Karla Schröder & Gonzalo Farias & Sebastián Dormido-Canto & Ernesto Fabregas, 2024. "Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution," Energies, MDPI, vol. 17(11), pages 1-13, June.