Short-Term Load Forecasting for Similar Days Based on PSO-SVM and Daily Feature Vector

doi:10.11930/j.issn.1004-9649.2013.7.91.3

Abstract

Abstract: The human body amenity indicator was introduced to make a comprehensive analysis of the influence of the meteorological factors on power load, and three main influence factors, including week type, daily weather type and date difference, were added to constitute the daily feature vector. By using the method for calculating the similarity degree to select similar days, the PSO-SVM forecasting model was built up with the daily feature vector and load data of the similar days. An forecasting analysis of the EUNITE load prediction competition data in 2001 shows that this method has a good adaptability, and can easily select the suitable similar days, and has a high prediction accuracy and good potential for promotion.

Key words: human body amenity indicator, daily feature vector, similar days, SVM, short-term load forecast

CLC Number:

TM715

CHEN Chao, HUANG Guo-yong, SHAO Zong-kai, WANG Xiao-dong, FAN Yu-gang. Short-Term Load Forecasting for Similar Days Based on PSO-SVM and Daily Feature Vector[J]. Electric Power, 2013, 46(7): 91-94.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2013.7.91.3

https://www.electricpower.com.cn/EN/Y2013/V46/I7/91

References

[1] 康重庆,夏清,刘梅. 电力系统负荷预测[M]. 北京：中国电力出版社,2007.
KANG Chong-qing, XIA Qing, LIU Mei. Power system load forecasting [M]. Beijing: China Electric Power Press, 2007.
[2] 李鑫滨,张娟,张岩,等. 基于D-S证据理论的相似日支持向量机短期负荷预测[J]. 电网技术,2010,34（7）：143-147.
LI Xin-bin, ZHANG Juan, ZHANG Yan, et al . Short-term load forecasting for similar days based on support vector machine and dempster-shafer theory[J]. Power System Technology, 2010, 34(7):143-147.
[3] 金义雄,段建民,徐进,等. 考虑气象因素的相似聚类短期负荷组合预测方法[J]. 电网技术,2007,31（19）,60-64.
JIN Yi-xiong, DUAN Jian-min, XU Jin, et al . A combinational short-term load forecasting method by use of similarity clustering and considering weather factors[J]. Power System Technology, 2007, 31(19): 60-64.
[4] 杨正瓴,田勇,张广涛,等. 相似日短期负荷预测的非线性理论基础与改进[J]. 电网技术,2006,30（6）：63-66.
YANG Zheng-ling, TIAN Yong, ZHANG Guang-tao, et al .Nonlinear theoretical foundation and improvement of similar days method for short term load forecasting[J]. Power System Technology, 2006, 30(6): 63-66.
[5] 黎灿兵,李晓辉,赵瑞,等. 电力短期负荷预测相似日选取算法[J]. 电力系统自动化,2008,32（9）：69-73.
LI Can-bing, LI Xiao-hui, ZHAO Rui, et al . A novel algorithm of selecting similar days for short-term power load forecasting[J]. Automation of Electric Power Systems, 2008, 32(9): 69-73.
[6] 秦海超,王玮,周晖,等. 人体舒适度指数在短期电力负荷预测中的应用[J]. 电力系统及其自动化学报,2006,18（2）：63-66.
QIN Hai-chao, WANG Wei, ZHOU Hui, et al . Short term electric load forecast using human body amenity indicator[J]. Proceedings of the CSU-EPSA, 2006, 18(2): 63-66.
[7] 祝燕萍,方鸽飞. 基于动态自适应神经网络和人体舒适度的短期负荷预测[J]. 电力系统保护与控制,2012,40（1）：57-61.
ZHU Yan-ping, FANG Ge-fei. Short-term load forecasting based on dynamic adaptive artificial neural network and human body amenity indicator[J]. Power System Protection and Control, 2012, 40(1): 57-61.
[8] 康重庆,程旭,夏清,等. 一种规范化处理相关因素的短期负荷预测新策略[J]. 电力系统自动化,1999,23（18）：32-35.
KANG Chong-qing, CHENG Xu, XIA Qing, et al . A new unified approach to short-term load forecasting considering correlated factors[J]. Automation of Electric Power Systems, 1999, 23(18):32-35.
[9] 王晶,冯显时,郭红珍. 基于蚁群元胞自动机理论的城市饱和负荷预测[J]. 中国电力,2011,44（7）：17-20.
WANG Jing, FENG Xian-shi, GUO Hong-zhen. Urban load saturation forecast based on ant cellular automata theory[J].Electric Power, 2011, 44(7): 17-20.
[10] East-Slovakia Power Distribution Company.World wide competition within the EUNITE network, EUNITE competition report[R].

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