Open Access
Issue |
Wuhan Univ. J. Nat. Sci.
Volume 28, Number 3, June 2023
|
|
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Page(s) | 223 - 236 | |
DOI | https://doi.org/10.1051/wujns/2023283223 | |
Published online | 13 July 2023 |
- Fan C, Xiao F, Zhao Y. A short-term building cooling load prediction method using deep learning algorithms[J]. Applied Energy, 2017, 195: 222-233. [CrossRef] [Google Scholar]
- Hochreiter S, Schnidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. [CrossRef] [Google Scholar]
- Song S L, Huang H T, Ruan T X. Abstractive text summarization using LSTM-CNN based deep learning[J]. Multimedia Tools and Applications, 2019, 78(1): 857-875. [CrossRef] [Google Scholar]
- Zheng J, Xu C, Zhang Z, et al. Electric load forecasting in smart grid using long short- term-memory based recurrent neural network[C]// 2017 51st Annual Conference on Information Sciences and Systems (CISS) . Baltimore: IEEE Press, 2017: 1-6. [Google Scholar]
- Marino D L, Amarasinghe K, Manic M. Building energy load forecasting using deep neural networks[C]// IECON 2016—42nd Annual Conference of the IEEE Industrial Electronics Society. Florence: IEEE Press, 2016: 7046-7051. [Google Scholar]
- Makridakis S, Spiliotis E, Assimakopoulos V. Statistical and machine learning forecasting methods: Concerns and ways forward[J]. PloS One, 2018, 13(3): e0194889. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Zhang J, Wei Y, Li D, et al. Short term electricity load forecasting using a hybrid model[J]. Energy, 2018, 158: 774-781. [CrossRef] [Google Scholar]
- Kim J, Moon J, Hwang E, et al. Recurrent inception convolution neural network for multi short-term load forecasting[J]. Energy Build, 2019, 194: 328-341. [CrossRef] [Google Scholar]
- Laib O, Khadir M T, Mihaylova L. Toward efficient energy systems based on natural gas consumption prediction with LSTM recurrent neural networks[J]. Energy, 2019, 177: 530-542. [CrossRef] [Google Scholar]
- Shao B L, Shi Y B, Zhao Y. Research on building energy consumption prediction model by integrating attention mechanism and LSTM [J]. Software Guide, 2021, 20(10): 61-67 (Ch). [Google Scholar]
- Zhao A J, Yang H J, Jing J, et al. A cold load prediction method of shopping malls oriented to functional Zoning[J]. Journal of Chongqing University, 2022(1): 1-15 (Ch). [Google Scholar]
- Xiao C, Chen N, Hu C, et al. Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach[J]. Remote Sensing of Environment, 2019, 233: 111358. [NASA ADS] [CrossRef] [Google Scholar]
- Cao J, Li Z, Li J. Financial time series forecasting model based on CEEMDAN and LSTM[J]. Physica A: Statistical Mechanics and Its Applications, 2019, 519: 127-139. [NASA ADS] [CrossRef] [Google Scholar]
- Xie H X, Chen F F, Liu Y, et al. Short-term power load prediction based on improved grey relational analysis and CMPSO-LSSVM algorithm[J]. Modern Electronics Technique, 2021, 44(8): 177-181 (Ch). [Google Scholar]
- Tian H H, Han A Y, Yu L T, et al. Research on multi-load short-term forecasting model of regional integrated energy system based on GRA-LSTM neural network[J]. Guangdong Electric Power, 2020, 33(5): 44-51 (Ch). [Google Scholar]
- Zhou Y J, Dou Z C, Ge S W, et al. Dynamic personalized search based on RNN with attention mechanism[J]. Chinese Journal of Computers, 2020, 43(5): 812-826 (Ch). [Google Scholar]
- Kong W, Dong Z Y, Jia Y, et al. Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid, 2017, 10(1): 841-851. [Google Scholar]
- Zhang J, Wei Y, Li D, et al. Short term electricity load forecasting using a hybrid model[J]. Energy, 2018, 158: 774-781. [CrossRef] [Google Scholar]
- Jain R K, Smith K M, Culligan P J, et al. Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy[J]. Applied Energy, 2014, 123: 168-178. [CrossRef] [Google Scholar]
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