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A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network

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  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. Park, Musik & Wang, Zhiyuan & Li, Lanyu & Wang, Xiaonan, 2023. "Multi-objective building energy system optimization considering EV infrastructure," Applied Energy, Elsevier, vol. 332(C).
  3. Mazhar Ali & Ankit Kumar Singh & Ajit Kumar & Syed Saqib Ali & Bong Jun Choi, 2023. "Comparative Analysis of Data-Driven Algorithms for Building Energy Planning via Federated Learning," Energies, MDPI, vol. 16(18), pages 1-18, September.
  4. Gu, Yewen & Goez, Julio C. & Mario, Guajardo & Wallace, Stein W., 2019. "Autonomous vessels: State of the art and potential opportunities in logistics," Discussion Papers 2019/6, Norwegian School of Economics, Department of Business and Management Science.
  5. Bing Han & Xiaohui Yang & Yafeng Ren & Wanggui Lan, 2019. "Comparisons of different deep learning-based methods on fault diagnosis for geared system," International Journal of Distributed Sensor Networks, , vol. 15(11), pages 15501477198, November.
  6. Michał Sabat & Dariusz Baczyński, 2021. "Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case," Energies, MDPI, vol. 14(11), pages 1-19, May.
  7. Odin Foldvik Eikeland & Filippo Maria Bianchi & Harry Apostoleris & Morten Hansen & Yu-Cheng Chiou & Matteo Chiesa, 2021. "Predicting Energy Demand in Semi-Remote Arctic Locations," Energies, MDPI, vol. 14(4), pages 1-17, February.
  8. Yang, Xiyun & Zhang, Yanfeng & Lv, Wei & Wang, Dong, 2021. "Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier," Renewable Energy, Elsevier, vol. 163(C), pages 386-397.
  9. Xie, Guangrui & Chen, Xi & Weng, Yang, 2020. "Input modeling and uncertainty quantification for improving volatile residential load forecasting," Energy, Elsevier, vol. 211(C).
  10. Bo Hu & Jian Xu & Zuoxia Xing & Pengfei Zhang & Jia Cui & Jinglu Liu, 2022. "Short-Term Combined Forecasting Method of Park Load Based on CEEMD-MLR-LSSVR-SBO," Energies, MDPI, vol. 15(8), pages 1-14, April.
  11. Longjin Lv & Lihua Luo & Yueping Yang, 2022. "Distribution Line Load Predicting and Heavy Overload Warning Model Based on Prophet Method," Sustainability, MDPI, vol. 14(21), pages 1-10, October.
  12. Jun Zheng & Bin Dou & Zilong Li & Tianyu Wu & Hong Tian & Guodong Cui, 2021. "Design and Analysis of a While-Drilling Energy-Harvesting Device Based on Piezoelectric Effect," Energies, MDPI, vol. 14(5), pages 1-15, February.
  13. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
  14. Juncheng Wang & Bin Zou & Mingfang Liu & Yishang Li & Hongjian Ding & Kai Xue, 2021. "Milling force prediction model based on transfer learning and neural network," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 947-956, April.
  15. Bulent Haznedar & Huseyin Cagan Kilinc & Furkan Ozkan & Adem Yurtsever, 2023. "Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 117(1), pages 681-701, May.
  16. Happy Aprillia & Hong-Tzer Yang & Chao-Ming Huang, 2020. "Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm," Energies, MDPI, vol. 13(8), pages 1-20, April.
  17. Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.
  18. Xihui Chen & Liping Peng & Gang Cheng & Chengming Luo, 2019. "Research on Degradation State Recognition of Planetary Gear Based on Multiscale Information Dimension of SSD and CNN," Complexity, Hindawi, vol. 2019, pages 1-12, March.
  19. Sholeh Hadi Pramono & Mahdin Rohmatillah & Eka Maulana & Rini Nur Hasanah & Fakhriy Hario, 2019. "Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System," Energies, MDPI, vol. 12(17), pages 1-16, August.
  20. Rondik J.Hassan & Adnan Mohsin Abdulazeez, 2021. "Deep Learning Convolutional Neural Network for Face Recognition: A Review," International Journal of Science and Business, IJSAB International, vol. 5(2), pages 114-127.
  21. Giacomo Talluri & Gabriele Maria Lozito & Francesco Grasso & Carlos Iturrino Garcia & Antonio Luchetta, 2021. "Optimal Battery Energy Storage System Scheduling within Renewable Energy Communities," Energies, MDPI, vol. 14(24), pages 1-23, December.
  22. Akash Koppa & Dominik Rains & Petra Hulsman & Rafael Poyatos & Diego G. Miralles, 2022. "A deep learning-based hybrid model of global terrestrial evaporation," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  23. Xin Feng & Qiang Feng & Shaohui Li & Xingwei Hou & Shugui Liu, 2020. "A Deep-Learning-Based Oil-Well-Testing Stage Interpretation Model Integrating Multi-Feature Extraction Methods," Energies, MDPI, vol. 13(8), pages 1-18, April.
  24. Fei Teng & Yafei Song & Xinpeng Guo, 2021. "Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model," Mathematics, MDPI, vol. 9(19), pages 1-21, September.
  25. Peng Liu & Peijun Zheng & Ziyu Chen, 2019. "Deep Learning with Stacked Denoising Auto-Encoder for Short-Term Electric Load Forecasting," Energies, MDPI, vol. 12(12), pages 1-15, June.
  26. Nasir Ayub & Muhammad Irfan & Muhammad Awais & Usman Ali & Tariq Ali & Mohammed Hamdi & Abdullah Alghamdi & Fazal Muhammad, 2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler," Energies, MDPI, vol. 13(19), pages 1-21, October.
  27. Dimitrios Kontogiannis & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2021. "A Meta-Modeling Power Consumption Forecasting Approach Combining Client Similarity and Causality," Energies, MDPI, vol. 14(19), pages 1-19, September.
  28. Davut Solyali, 2020. "A Comparative Analysis of Machine Learning Approaches for Short-/Long-Term Electricity Load Forecasting in Cyprus," Sustainability, MDPI, vol. 12(9), pages 1-34, April.
  29. M. Nagaraju & Priyanka Chawla, 2020. "Systematic review of deep learning techniques in plant disease detection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(3), pages 547-560, June.
  30. Kanitta Yarak & Apichon Witayangkurn & Kunnaree Kritiyutanont & Chomchanok Arunplod & Ryosuke Shibasaki, 2021. "Oil Palm Tree Detection and Health Classification on High-Resolution Imagery Using Deep Learning," Agriculture, MDPI, vol. 11(2), pages 1-16, February.
  31. Ivana Kiprijanovska & Simon Stankoski & Igor Ilievski & Slobodan Jovanovski & Matjaž Gams & Hristijan Gjoreski, 2020. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning," Energies, MDPI, vol. 13(10), pages 1-29, May.
  32. Asif Khan & Hyunho Hwang & Heung Soo Kim, 2021. "Synthetic Data Augmentation and Deep Learning for the Fault Diagnosis of Rotating Machines," Mathematics, MDPI, vol. 9(18), pages 1-26, September.
  33. 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).
  34. 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.
  35. Gillmann, Niels & Kim, Alisa, 2021. "Quantification of Economic Uncertainty: a deep learning approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242421, Verein für Socialpolitik / German Economic Association.
  36. Stanislaw Osowski & Robert Szmurlo & Krzysztof Siwek & Tomasz Ciechulski, 2022. "Neural Approaches to Short-Time Load Forecasting in Power Systems—A Comparative Study," Energies, MDPI, vol. 15(9), pages 1-21, April.
  37. Roberto Casado-Vara & Angel Martin del Rey & Daniel Pérez-Palau & Luis de-la-Fuente-Valentín & Juan M. Corchado, 2021. "Web Traffic Time Series Forecasting Using LSTM Neural Networks with Distributed Asynchronous Training," Mathematics, MDPI, vol. 9(4), pages 1-21, February.
  38. Terrén-Serrano, Guillermo & Martínez-Ramón, Manel, 2021. "Comparative analysis of methods for cloud segmentation in ground-based infrared images," Renewable Energy, Elsevier, vol. 175(C), pages 1025-1040.
  39. Kun Liang & Jingjing Liu & Yiying Zhang, 2021. "The Effects of Non-Directional Online Behavior on Students’ Learning Performance: A User Profile Based Analysis Method," Future Internet, MDPI, vol. 13(8), pages 1-14, July.
  40. Fei Liao & Liangli Ma & Jingjing Pei & Linshan Tan, 2019. "Combined Self-Attention Mechanism for Chinese Named Entity Recognition in Military," Future Internet, MDPI, vol. 11(8), pages 1-11, August.
  41. Yih-Der Lee & Jheng-Lun Jiang & Yuan-Hsiang Ho & Wei-Chen Lin & Hsin-Ching Chih & Wei-Tzer Huang, 2020. "Neutral Current Reduction in Three-Phase Four-Wire Distribution Feeders by Optimal Phase Arrangement Based on a Full-Scale Net Load Model Derived from the FTU Data," Energies, MDPI, vol. 13(7), pages 1-20, April.
  42. Yueyun Shang & Shunzhi Jiang & Dengpan Ye & Jiaqing Huang, 2020. "Enhancing the Security of Deep Learning Steganography via Adversarial Examples," Mathematics, MDPI, vol. 8(9), pages 1-10, August.
  43. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
  44. Hongwei Wang & Yuansheng Huang & Chong Gao & Yuqing Jiang, 2019. "Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network," Energies, MDPI, vol. 12(16), pages 1-21, August.
  45. M. Nagaraju & Priyanka Chawla, 0. "Systematic review of deep learning techniques in plant disease detection," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 0, pages 1-14.
  46. Shivani Sinha & Abhishek Anand & Rajvardhan Azad, 2020. "Prospects of Artificial Intelligence in Ophthalmic Practice," Biomedical Journal of Scientific & Technical Research, Biomedical Research Network+, LLC, vol. 27(5), pages 21159-21166, May.
  47. Yan Xiao & Congdong Li & Vincenzo Liu, 2022. "DFM-GCN: A Multi-Task Learning Recommendation Based on a Deep Graph Neural Network," Mathematics, MDPI, vol. 10(5), pages 1-14, February.
  48. Ali Mokhtar & Nadhir Al-Ansari & Wessam El-Ssawy & Renata Graf & Pouya Aghelpour & Hongming He & Salma M. Hafez & Mohamed Abuarab, 2023. "Prediction of Irrigation Water Requirements for Green Beans-Based Machine Learning Algorithm Models in Arid Region," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1557-1580, March.
  49. Bin Wang & Enhui Wang & Zikun Zhu & Yangyang Sun & Yaodong Tao & Wei Wang, 2021. "An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors," International Journal of Distributed Sensor Networks, , vol. 17(10), pages 15501477211, October.
  50. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
  51. Tomasz Ciechulski & Stanisław Osowski, 2020. "Deep Learning Approach to Power Demand Forecasting in Polish Power System," Energies, MDPI, vol. 13(22), pages 1-13, November.
  52. Gangjun Gong & Xiaonan An & Nawaraj Kumar Mahato & Shuyan Sun & Si Chen & Yafeng Wen, 2019. "Research on Short-Term Load Prediction Based on Seq2seq Model," Energies, MDPI, vol. 12(16), pages 1-18, August.
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  54. Yosui Miyazaki & Yusuke Kameda & Junji Kondoh, 2019. "A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets," Energies, MDPI, vol. 12(24), pages 1-14, December.
  55. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
  56. Can, Umit & Alatas, Bilal, 2019. "A new direction in social network analysis: Online social network analysis problems and applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
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  59. Hang Zhao & Jun Zhang & Xiaohui Wang & Hongxia Yuan & Tianlu Gao & Chenxi Hu & Jing Yan, 2021. "The Economy and Policy Incorporated Computing System for Social Energy and Power Consumption Analysis," Sustainability, MDPI, vol. 13(18), pages 1-18, September.
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