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ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (2): 98-108.doi: 10.12204/j.issn.1000-7229.2022.02.012
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LIU Wenjie(), LIU He(), WANG Yingnan(), YANG Guotian(), LI Xinli()
Received:
2021-04-29
Online:
2022-02-01
Published:
2022-03-24
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CLC Number:
LIU Wenjie, LIU He, WANG Yingnan, YANG Guotian, LI Xinli. Short-Term Power Load Forecasting Method Based on CEEMDAN and LSTM-Attention Network[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(2): 98-108.
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[1] | 廖旎焕, 胡智宏, 马莹莹, 等. 电力系统短期负荷预测方法综述[J]. 电力系统保护与控制, 2011, 39(1):147-152. |
LIAO Nihuan, HU Zhihong, MA Yingying, et al. Review of the short-term load forecasting methods of electric power system[J]. Power System Protection and Control, 2011, 39(1):147-152. | |
[2] |
VAGHEFI A, JAFARI M A, BISSE E, et al. Modeling and forecasting of cooling and electricity load demand[J]. Applied Energy, 2014, 136:186-196.
doi: 10.1016/j.apenergy.2014.09.004 URL |
[3] | 陈红. 电力系统短期负荷预测系统的实现[J]. 电力系统自动化, 1997, 21(12):58-60. |
CHEN Hong. Implementation of a short-term forecasting system[J]. Automation of Electric Power Systems, 1997, 21(12):58-60. | |
[4] |
LEE C M, KO C N. Short-term load forecasting using lifting scheme and ARIMA models[J]. Expert Systems With Applications, 2011, 38(5):5902-5911.
doi: 10.1016/j.eswa.2010.11.033 URL |
[5] | 蒋敏, 顾东健, 孔军, 等. 基于在线序列极限支持向量回归的短期负荷预测模型[J]. 电网技术, 2018, 42(7):2240-2247. |
JIANG Min, GU Dongjian, KONG Jun, et al. Short-term load forecasting model based on online sequential extreme support vector regression[J]. Power System Technology, 2018, 42(7):2240-2247. | |
[6] |
DAGDOUGUI H, BAGHERI F, LE H, et al. Neural network model for short-term and very-short-term load forecasting in district buildings[J]. Energy and Buildings, 2019, 203:109408.
doi: 10.1016/j.enbuild.2019.109408 URL |
[7] |
GHANBARI A, KAZEMI S M R, MEHMANPAZIR F, et al. A cooperative ant colony optimization-genetic algorithm approach for construction of energy demand forecasting knowledge-based expert systems[J]. Knowledge-Based Systems, 2013, 39:194-206.
doi: 10.1016/j.knosys.2012.10.017 URL |
[8] | 史会峰, 牛东晓, 卢艳霞. 基于贝叶斯神经网络短期负荷预测模型[J]. 中国管理科学, 2012, 20(4):118-124. |
SHI Huifeng, NIU Dongxiao, LU Yanxia. The short-term load forecasting model based on Bayesian neural network[J]. Chinese Journal of Management Science, 2012, 20(4):118-124. | |
[9] | 周佃民, 管晓宏, 孙婕, 等. 基于神经网络的电力系统短期负荷预测研究[J]. 电网技术, 2002, 26(2):10-13,18. |
ZHOU Dianmin, GUAN Xiaohong, SUN Jie, et al. A short-term load forecasting system based on BP artificial neural network[J]. Power System Technology, 2002, 26(2):10-13,18. | |
[10] | 孔祥玉, 郑锋, 鄂志君, 等. 基于深度信念网络的短期负荷预测方法[J]. 电力系统自动化, 2018, 42(5):133-139. |
KONG Xiangyu, ZHENG Feng, E Zhijun, et al. Short-term load forecasting based on deep belief network[J]. Automation of Electric Power Systems, 2018, 42(5):133-139. | |
[11] | HOSSEN T, PLATHOTTAM S J, ANGAMUTHU R K, et al. Short-term load forecasting using deep neural networks (DNN)[C]// 2017 North American Power Symposium (NAPS). Morgantown, WV, USA: IEEE, 2017: 1-6. |
[12] | 姚程文, 杨苹, 刘泽健. 基于CNN-GRU混合神经网络的负荷预测方法[J]. 电网技术, 2020, 44(9):3416-3424. |
YAO Chengwen, YANG Ping, LIU Zejian. Load forecasting method based on CNN-GRU hybrid neural network[J]. Power System Technology, 2020, 44(9):3416-3424. | |
[13] | 赵兵, 王增平, 纪维佳, 等. 基于注意力机制的CNN-GRU短期电力负荷预测方法[J]. 电网技术, 2019, 43(12):4370-4376. |
ZHAO Bing, WANG Zengping, JI Weijia, et al. A short-term power load forecasting method based on attention mechanism of CNN-GRU[J]. Power System Technology, 2019, 43(12):4370-4376. | |
[14] |
RAHMAN A, SRIKUMAR V, SMITH A D. Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks[J]. Applied Energy, 2018, 212:372-385.
doi: 10.1016/j.apenergy.2017.12.051 URL |
[15] |
KONG W C, DONG Z Y, JIA Y W, et al. Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid, 2019, 10(1):841-851.
doi: 10.1109/TSG.2017.2753802 URL |
[16] | 彭文, 王金睿, 尹山青. 电力市场中基于Attention-LSTM的短期负荷预测模型[J]. 电网技术, 2019, 43(5):1745-1751. |
PENG Wen, WANG Jinrui, YIN Shanqing. Short-term load forecasting model based on attention-LSTM in electricity market[J]. Power System Technology, 2019, 43(5):1745-1751. | |
[17] | 康重庆. 电力系统负荷预测[M]. 北京: 中国电力出版社, 2017. |
[18] | HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971):903-995. |
[19] | 孔祥玉, 李闯, 郑锋, 等. 基于经验模态分解与特征相关分析的短期负荷预测方法[J]. 电力系统自动化, 2019, 43(5):46-52. |
KONG Xiangyu, LI Chuang, ZHENG Feng, et al. Short-term load forecasting method based on empirical mode decomposition and feature correlation analysis[J]. Automation of Electric Power Systems, 2019, 43(5):46-52. | |
[20] | 邓带雨, 李坚, 张真源, 等. 基于EEMD-GRU-MLR的短期电力负荷预测[J]. 电网技术, 2020, 44(2):593-602. |
DENG Daiyu, LI Jian, ZHANG Zhenyuan, et al. Short-term electric load forecasting based on EEMD-GRU-MLR[J]. Power System Technology, 2020, 44(2):593-602. | |
[21] |
YEH J R, SHIEH J S, HUANG N E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2(2):135-156.
doi: 10.1142/S1793536910000422 URL |
[22] |
ZHAO J P, HUANG D J. Mirror extending and circular spline function for empirical mode decomposition method[J]. Journal of Zhejiang University-SCIENCE A, 2001, 2(3):247-252.
doi: 10.1631/jzus.2001.0247 URL |
[23] |
白春华, 周宣赤, 林大超, 等. 消除EMD端点效应的PSO-SVM方法研究[J]. 系统工程理论与实践, 2013, 33(5):1298-1306.
doi: 10.12011/1000-6788(2013)5-1298 |
BAI Chunhua, ZHOU Xuanchi, LIN Dachao, et al. PSO-SVM method based on elimination of end effects in EMD[J]. Systems Engineering-Theory & Practice, 2013, 33(5):1298-1306. | |
[24] | 林丽, 周霆, 余轮. EMD算法中边界效应处理技术[J]. 计算机工程, 2009, 35(23):265-268. |
LIN Li, ZHOU Ting, YU Lun. Edge effect processing technique in EMD algorithm[J]. Computer Engineering, 2009, 35(23):265-268. | |
[25] |
HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780.
doi: 10.1162/neco.1997.9.8.1735 URL |
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