热门出版物 - 学术资源搜索

热门出版物

我的图书馆

引用“”的文章Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network

标题/作者	年份
Predicting residential energy consumption using CNN-LSTM neural networks TY Kim, SB Cho Energy 182, 72-81	2019
Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions L Alzubaidi, J Zhang, AJ Humaidi, A Al-Dujaili, Y Duan, O Al-Shamma, ... Journal of big Data 8 (1), 1-74	2021
Probabilistic individual load forecasting using pinball loss guided LSTM Y Wang, D Gan, M Sun, N Zhang, Z Lu, C Kang Applied Energy 235, 10-20	2019
Model-free real-time EV charging scheduling based on deep reinforcement learning Z Wan, H Li, H He, D Prokhorov IEEE Transactions on Smart Grid 10 (5), 5246-5257	2018
Big data analytics in smart grids: a review Y Zhang, T Huang, EF Bompard Energy informatics 1 (1), 1-24	2018
A review of deep learning with special emphasis on architectures, applications and recent trends S Sengupta, S Basak, P Saikia, S Paul, V Tsalavoutis, F Atiah, V Ravi, ... Knowledge-Based Systems 194, 105596	2020
Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions SN Fallah, RC Deo, M Shojafar, M Conti, S Shamshirband Energies 11 (3), 596	2018
A novel combined prediction scheme based on CNN and LSTM for urban PM 2.5 concentration D Qin, J Yu, G Zou, R Yong, Q Zhao, B Zhang IEEE Access 7, 20050-20059	2019
Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting S Wang, X Wang, S Wang, D Wang International Journal of Electrical Power & Energy Systems 109, 470-479	2019
Multi-scale convolutional neural network with time-cognition for multi-step short-term load forecasting Z Deng, B Wang, Y Xu, T Xu, C Liu, Z Zhu IEEE Access 7, 88058-88071	2019
Empirical mode decomposition based deep learning for electricity demand forecasting J Bedi, D Toshniwal IEEE access 6, 49144-49156	2018
Recurrent inception convolution neural network for multi short-term load forecasting J Kim, J Moon, E Hwang, P Kang Energy and buildings 194, 328-341	2019
A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model N Mohan, KP Soman, SS Kumar Applied energy 232, 229-244	2018
A novel CNN-GRU-based hybrid approach for short-term residential load forecasting M Sajjad, ZA Khan, A Ullah, T Hussain, W Ullah, MY Lee, SW Baik Ieee Access 8, 143759-143768	2020
Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network S Han, Y Qiao, J Yan, Y Liu, L Li, Z Wang Applied energy 239, 181-191	2019
Using a stacked residual LSTM model for sentiment intensity prediction J Wang, B Peng, X Zhang Neurocomputing 322, 93-101	2018
Multi-step short-term power consumption forecasting with a hybrid deep learning strategy K Yan, X Wang, Y Du, N Jin, H Huang, H Zhou Energies 11 (11), 3089	2018
A hybrid LSTM neural network for energy consumption forecasting of individual households K Yan, W Li, Z Ji, M Qi, Y Du Ieee Access 7, 157633-157642	2019
Hybrid short-term load forecasting scheme using random forest and multilayer perceptron J Moon, Y Kim, M Son, E Hwang Energies 11 (12), 3283	2018
Deep learning for daily peak load forecasting–a novel gated recurrent neural network combining dynamic time warping Z Yu, Z Niu, W Tang, Q Wu IEEE access 7, 17184-17194	2019

1 - 20

计算机程序会自动估算并确定日期和引用次数。

共建清朗的网络空间,如遇有害信息,请举报。
本站数据皆整合自互联网公开资源索引,方便科研学术方面查询,并不存储相关数据资源;如对此有异议,请联系我们解决.
© 2023 学术资源搜索 @联系我们 | 申请短期会员 | 数据源提交