Short-term Power Load Forecasting Based on Deep Forest Algorithm
CHEN Lüpeng1,2, YIN Linfei3, YU Tao1,2, WANG Keying1,2
1. College of Electric Power Engineering, South China University of Technology, Guangzhou 510640, China;2. Guangdong Key Laboratory of Clean Energy Technology, Guangzhou 510640, China;3. College of Electrical Engineering, Guangxi University, Nanning 530004, China
Online:2018-11-01
Supported by:
This work is supported by National Natural Science Foundation of China(No. 51777078,No. 51477055).
CHEN Lüpeng, YIN Linfei, YU Tao, WANG Keying. Short-term Power Load Forecasting Based on Deep Forest Algorithm[J]. Electric Power Construction, 2018, 39(11): 42-50.
[1]康重庆, 夏清, 张伯明. 电力系统负荷预测研究综述与发展方向的探讨[J]. 电力系统自动化, 2004, 28(17): 1-11.
KANG Chongqing, XIA Qing, ZHANG Boming. Review of power system load forecasting and its development[J]. Automation of Electric Power Systems, 2004, 28(17): 1-11.
[2]万昆, 柳瑞禹. 区间时间序列向量自回归模型在短期电力负荷预测中的应用[J]. 电网技术, 2012, 36(11): 77-81.
WAN Kun, LIU Ruiyu. Application of interval time-series vector autoregressive model in short-term load forecasting[J]. Power System Technology, 2012, 36(11): 77-81.
[3]吴潇雨, 和敬涵, 张沛, 等. 基于灰色投影改进随机森林算法的电力系统短期负荷预测[J]. 电力系统自动化, 2015, 39(12): 50-55.
WU Xiaoyu, HE Jinghan, ZHANG Pei, et al. Power system short-term load forecasting based on improved random forest with grey relation projection[J]. Automation of Electric Power Systems, 2015, 39(12): 50-55.
[4]吴倩红, 高军, 侯广松, 等. 实现影响因素多源异构融合的短期负荷预测支持向量机算法[J]. 电力系统自动化, 2016, 40(15): 67-72, 92.
WU Qianhong, GAO Jun, HOU Guangsong, et al. Short-term load forecasting support vector machine algorithm based on multi-source heterogeneous fusion of load factors[J]. Automation of Electric Power Systems, 2016, 40(15): 67-72,92.
[5]肖白, 聂鹏, 穆钢, 等. 基于多级聚类分析和支持向量机的空间负荷预测方法[J]. 电力系统自动化, 2015, 39(12):56-61.
XIAO Bai, NIE Peng, MU Gang, et al. A spatial load forecasting method based on multilevel clustering analysis and support vector machine[J]. Automation of Electric Power Systems, 2015, 39(12):56-61.
[6]李龙, 魏靖, 黎灿兵, 等. 基于人工神经网络的负荷模型预测[J]. 电工技术学报, 2015, 30(8): 225-230.
LI Long, WEI Jing, LI Canbing, et al. Prediction of load model based on artificial neural network[J]. Transactions of China Electrotechnical Society, 2015, 30(8): 225-230.
[7]郭丽丽, 丁世飞. 深度学习研究进展[J]. 计算机科学, 2015, 42(5): 28-33.
GUO Lili, DING Shifei. Research progress on deep learning[J]. Computer Science, 2015, 42(5): 28-33.
[8]张刚, 刘福潮, 王维洲, 等. 电网短期负荷预测的BP-ANN方法及应用[J]. 电力建设, 2014, 35(3): 54-58.
ZHANG Gang, LIU Fuchao, WANG Weizhou, et al. BP-ANN method for power grid short-term load forecasting and its application[J]. Electric Power Construction, 2014, 35(3): 54-58.
[9]HU R, WEN S, ZENG Z, et al. A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm[J]. Neurocomputing, 2017, 221:24-31.
[10]HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
[11]张蕾, 章毅. 大数据分析的无限深度神经网络方法[J]. 计算机研究与发展, 2016, 53(1): 68-79.
ZHANG Lei, ZHANG Yi. Big data analysis by infinite deep neural networks[J]. Journal of Computer Research and Development, 2016, 53(1): 68-79.
[12]KRIEGESKORTE N. Deep neural networks: a new framework for modeling biological vision and brain information processing[J]. Annual Review of Vision Science, 2015, 1(1): 417.
[13]BENGIO Y, COURVILLE A, VINCENT P. Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798-1828.
[14]RYU S, NOH J, KIM H, et al. Deepneural network based demand side short term load forecasting[J]. Energies, 2017, 10(3): 1-20.
[15]刘威, 刘尚, 白润才, 等. 互学习神经网络训练方法研究[J]. 计算机学报, 2017, 40(6): 1291-1308.
LIU Wei, LIU Shang, BAI Runcai, et al. Research of mutual learning neural network training method[J]. Chinese Journal of Computers, 2017, 40(6): 1291-1308.
[16]SAINATH T N, KINGSBURY B, SINDHWANI V, et al. Low-rank matrix factorization for deep neural network training with high-dimensional output targets[C]// IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver: IEEE, 2013: 6655-6659.
[17]ZHOU Z H, FENG J. Deep Forest: towards an alternative to deep neural networks[EB/OL].(2017-02-28)[2018-06-02]. https://arxiv.org/abs/1702.08835.
[18]黄小庆, 陈颉, 陈永新, 等. 大数据背景下的充电站负荷预测方法[J]. 电力系统自动化, 2016, 40(12): 68-74.
HUANG Xiaoqing, CHEN Jie, CHEN Yongxin, et al. Load forecasting method for electric vehicle charging station based on big data[J]. Automation of Electric Power Systems, 2016, 40(12): 68-74.
[19]王惠中, 周佳, 刘轲. 电力系统短期负荷预测方法研究综述[J]. 电气自动化, 2015, 37(1): 1-3,39.
WANG Huizhong, ZHOU Jia, LIU Ke. Summary of research on the short-term load forecasting method of the electric power system[J]. Electrical Automation, 2015, 37(1): 1-3,39.
[20]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.
[21]BREIMAN L. Bagging predictors[J]. Machine learning, 1996, 24(2): 123-140.
[22]HO T K. The random subspace method for constructing decision forests[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(8): 832-844.
[23]赵腾, 王林童, 张焰, 等. 采用互信息与随机森林算法的用户用电关联因素辨识及用电量预测方法[J]. 中国电机工程学报, 2016, 36(3): 604-614.
ZHAO Teng, WANG Lintong, ZHANG Yan, et al. Relation factor identification of electricity consumption behavior of users and electricity demand forecasting based on mutual information and random forests[J]. Proceedings of the CSEE, 2016, 36(3): 604-614.
[24]喻金平, 黄细妹, 李康顺. 基于一种新的属性选择标准的ID3改进算法[J]. 计算机应用研究, 2012, 29(8): 2895-2898.
YU Jinping, HUANG Ximei, LI Kangshun. Improved ID3 algorithm based on new attributes selection criterion[J]. Application Research of Computers, 2012, 29(8): 2895-2885.
[25]MANTAS C J, ABELLN J, CASTELLANO J G. Analysis of credal-c4.5 for classification in noisy domains[J]. Expert Systems with Applications, 2016, 61: 314-326.
[26]GUO Y C. Knowledge-Enabled short-term load forecasting based on pattern-base using classification & regression tree and support vector regression[C]// Fifth International Conference on Natural Computation. Tianjin: IEEE, 2009: 425-429.
[27]王德文,孙志伟. 电力用户侧大数据分析与并行负荷预测[J]. 中国电机工程学报, 2015, 35(3): 527-537.
WANG Dewen, SUN Zhiwei. Big data analysis and parallel load forecasting of electric power user side[J]. Proceedings of the CSEE, 2015, 35(3): 527-537.
[28]CHO K, MERRINBOER B V, GULCEHRE C, et al. Learning phrase representations using RNN encode-decoder for statistical machine translation[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha: Springer Berlin Heidelberg, 2014: 1724-1734.
[29]GRAVES A, MOHAMED A R, HINTON G. Speech recognition with deep recurrent neural networks[C]// IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver: IEEE, 2013: 6645-6649.
[30]LIU F T, TING K M, Yu Y, et al. Spectrum of variable-random trees[J]. Journal of Artificial Intelligence Research, 2008, 32(1): 355-384.
[31]中国电机工程学会电工数学专委会. 第九届“中国电机工程学会杯”全国大学生电工数学建模竞赛题目[EB/OL].(2016-04-25)[2018-05-10].http://shumo.nedu.edu.cn.
[32]苏学能, 刘天琪, 曹鸿谦, 等. 基于Hadoop架构的多重分布式BP神经网络的短期负荷预测方法[J]. 中国电机工程学报, 2017, 37(17): 4966-4973.
SU Xueneng, LIU Tianqi, CAO Hongqian, et al. A multiple distributed BP Neural Networks approach for short-term load forecasting based on Hadoop framework[J]. Proceedings of the CSEE, 2017, 37(17): 4966-4973.
[33]黄青平, 李玉娇, 刘松, 等. 基于模糊聚类与随机森林的短期负荷预测[J]. 电测与仪表, 2017, 54(23): 41-46.
HUANG Qingping, LI Yujiao, LIU Song, et al. Short-time load forecasting based on fuzzy clustering and random forest[J]. Electrical Measurement & Instrumentation, 2017, 54(23): 41-46.
[34]TAIEB S B, HYNDMAN R J. A gradient boosting approach to the kaggle load forecasting competition[J]. International Journal of Forecasting, 2014, 30(2): 382-394.
[35]MANGALOVA E, SHESTERNEVA O. K-nearest neighbors for GEFCom2014 probabilistic wind power forecasting[J]. International Journal of Forecasting, 2016, 32(3):1067-1073.