Short-term Load Forecasting Based on CatBoost Algorithm

doi:10.11985/2020.01.011

Abstract

Abstract:

In order to improve the accuracy of short-term load forecasting in power systems, CatBoost algorithm is applied to short-term power load for prediction. Compared with the traditional load prediction deep neural network algorithm, the CatBoost algorithm does not need to perform numerical forced substitution in the data preprocessing process, and at the same time reduces the dependence on hyperparameters to obtain better prediction results. Firstly, 20 days of data from two regions are used to train the CatBoost model. Secondly, analyze the prediction results and compared with other common intelligent algorithms. Finally, the simulation results showed that the CatBoost algorithm has the ability to predict short-term load of the power system.

Key words: CatBoost, short-term load forecasting, conditional offset, hyperparameters adjustment, data preprocessing

CLC Number:

TM74

DANG Cunlu,WU Wencheng,LI Chaofeng,LI Yongqiang. Short-term Load Forecasting Based on CatBoost Algorithm[J]. Journal of Electrical Engineering, 2020, 15(1): 76-82.

Figures/Tables 5

References 23

[1]	靳现林, 赵迎春, 吴刚 . 考虑分布式光伏和电动汽车接入的配电网空间负荷预测方法[J]. 电力系统保护与控制, 2019,47(14):10-19.
	Jin Xianlin, Zhao Yingchun, Wu Gang . Distribution network spatial load forecasting method considering distributed photovoltaic and electric vehicle access[J]. Power System Protection and Control, 2019,47(14):10-19.
[2]	张丽, 张涛, 王福忠 , 等. 基于柔性负荷响应特性的超短期预测方法[J]. 电力系统保护与控制, 2019,47(9):27-34.
	Zhang Li, Zhang Tao, Wang Fuzhong , et al. Ultra-short term prediction method based on flexible load response characteristics[J]. Power System Protection and Control, 2019,47(9):27-34.
[3]	史文博, 王健, 沈添福 , 等. 基于改进倍比平滑法的节假日短期负荷预测方法[J]. 电力建设, 2014,35(10):21-25. doi: 10.3969/j.issn.1000-7229.2014.10.005
	Shi Wenbo, Wang Jian, Shen Tianfu , et al. Prediction method of short term load in holidays based on improved multiple proportions smoothing method[J]. Electric Power Construction, 2014,35(10):21-25. doi: 10.3969/j.issn.1000-7229.2014.10.005
[4]	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.
[5]	赵云, 高泽璞, 肖勇 , 等. 基于多层聚类和改进BP神经网络的短期负荷预测[J]. 武汉大学学报, 2019,52(7):622-629.
	Zhao Yun, Gao Zepu, Xiao Yong , et al. Short term load forecasting based on multi-layer clustering and improved BP neural network[J]. Engineering Journal of Wuhan University, 2019,52(7):622-629.
[6]	王程斯, 赵少东 . 区分待预测日的城市电网短期负荷预测方法研究[J]. 自动化与仪器仪表, 2019(6):114-117.
	Wang Chengsi, Zhao Shaodong . Research on short term load forecasting method for urban power grid on the forecast day[J]. Automation & Instrumentation, 2019(6):114-117.
[7]	闫云凤 . 基于决策森林的回归模型方法研究及应用[D]. 杭州:浙江大学, 2019.
[8]	吴云, 雷建文, 鲍丽山 , 等. 基于改进灰色关联分析与蝙蝠优化神经网络的短期负荷预测[J]. 电力系统自动化, 2018,42(20):67-74.
	Wu Yun, Lei Jianwen, Bao Lishan , et al. Short term load forecasting based on improved grey correlation analysis and bat optimization neural network[J]. Automation of Electric Power Systems, 2018,42(20):67-74.
[9]	王帅, 王文爽, 孙伟 , 等. 基于粗糙集和BP网络的微网短期负荷预测[J]. 控制工程, 2018,25(8):1528-1533.
	Wang Shuai, Wang Wenshuang, Sun Wei , et al. Short term load forecasting of microgrid based on rough set and BP network[J]. Control Engineering of China, 2018,25(8):1528-1533.
[10]	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. doi: 10.3969/j.issn.1000－7229.2014.03.010
	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. doi: 10.3969/j.issn.1000－7229.2014.03.010
[11]	李香龙, 张宝群, 张宇 , 等. 基于EEMD-BP神经网络的含电采暖的配电变压器短期负荷预测[J]. 电测与仪表, 2018,55(10):101-107.
	Li Xianglong, Zhang Baoqun, Zhang Yu , et al. Short term load forecasting of distribution transformer with electric heating based on EEMD-BP neural network[J]. Electrical Measurement & Instrumentation, 2018,55(10):101-107.
[12]	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. doi: DOI:10.7544/issn1000-1239.2016.20150663
	Zhang Lei, Zhang Yi . Infinite depth neural network method for big data analysis[J]. Journal of Computer Research and Development, 2016,53(1):68-79. doi: DOI:10.7544/issn1000-1239.2016.20150663
[13]	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.
[14]	邵亚洁 . 基于复合CatBoost模型的P2P网贷违约分类预测[D]. 兰州:兰州大学, 2019.
[15]	鲁迪, 王星华, 刘升伟 , 等. 加权多分位鲁棒ELM的短期负荷预测方法[J/OL]. 电力系统及其自动化学报:1-7[2019-06-17]. https://doi.org/10.19635/j.cnki.csu-epsa. 000266.
	Lu Di, Wang Xinghua, Liu Shengwei , et al. Weighted multi-component robust ELM short term load forecasting method[J/OL]. Proceedings of the CSU-EPSA:1-7 [2019-06-17]. https://doi.org/10.19635/j.cnki.csu-epsa. 000266.
[16]	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.
[17]	杨甲甲, 刘国龙, 赵俊华 , 等. 采用长短期记忆深度学习模型的工业负荷短期预测方法[J]. 电力建设, 2018,39(10):20-27.
	Yang Jiajia, Liu Guolong, Zhao Junhua , et al. A long short term memory based deep learning method for industrial load forecasting[J]. Electric Power Construction, 2018,39(10):20-27.
[18]	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. doi: 10.7667/j.issn.1674-3415.2011.01.028
	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 doi: 10.7667/j.issn.1674-3415.2011.01.028
[19]	刘思捷, 张海鹏, 林舜江 , 等. 夏季日最大降温负荷的估算和预测方法[J]. 电力系统保护与控制, 2016,44(5):75-81. doi: 10.7667/PSPC150822
	Liu Sijie, Zhang Haipeng, Lin Shunjiang , et al. An estimating and forecasting method for daily maximum cooling load in summer[J]. Power System Protection and Control, 2016,44(5):75-81. doi: 10.7667/PSPC150822
[20]	周志华 . 机器学习[M]. 北京: 清华大学出版社, 2016.
[21]	谢从珍, 王江储, 谢心昊 , 等. 基于细粒度特征的BOA- GBDT光伏出力预测[J/OL]. 电网技术:1-9 [2019-07-04]. https://doi.org/10.13335/j.1000-3673.pst.2019.0447.
	Xie Congzhen, Wang Jiangchu, Xie Xinhao , et al. Prediction of PVoutput based on fine-grained features of BOA-GBDT [J/OL]. Power System Technology:1-9 [2019-07-04]. https://doi.org/10.13335/j.1000-3673.pst.2019.0447.
[22]	Dorogush A V, Ershov V, Gulin A . CatBoost:Gradient boosting with categorical features support[M]. New York:Cornell University, 2018.
[23]	Wu Lifeng, Huang Guomin, Fan Junliang , et al. Potential of kernel-based nonlinear extension of ARPS decline model and gradient boosting with categorical features support for predicting daily global solar radiation in humid regions[J]. Energy Conversion and Management, 2019(183):280-295.

算法	地区1负荷预测误差指标			地区2负荷预测误差指标
算法	RMSE	MAE	MAPE	RMSE	MAE	MAPE
KNN算法	348.12	293.90	3.679	517.88	485.69	6.51
BP神经网络	423.30	322.44	5.650	497.51	466.21	6.77
决策树算法	510.52	452.72	5.860	403.65	348.12	4.37
GBDT算法	436.62	285.12	5.330	996.94	879.56	11.29
CatBoost算法	308.02	199.56	2.880	321.56	274.97	4.14