微网用户短期负荷预测相似日选择算法

doi:10.11930.2015.4.156

摘要/Abstract

摘要： 微网用户负荷基荷小、波动性和随机性大，增大了短期负荷预测难度。科学合理地选择相似日可以在一定程度上改善短期负荷预测的效果。分析了相似日选择的影响因素，针对微网用户负荷特点，提出了一种负荷点尺度上的相似日选取算法。该算法考虑了前几日气象因素的累积效应、短期负荷的连续性和周期性及时间距离的影响，其相似日评价函数计及了日特征相似和局部形相似，并引入时间因子，克服了传统人工经验选取相似日算法的主观性，使得选择的相似日更加客观合理。实例验证表明，该方法所选择的相似日用于微网用户短期预测时，可以提高预测精度，有一定的使用价值。

关键词: 微网, 短期负荷预测, 相似日, 日特征相似, 局部形相似, 时间因子

Abstract: The small base load, high fluctuation and randomness of the microgrid increase the difficulty of short-term load forecasting. Scientific and proper selection of similar days can improve to some extent the effectiveness of the short-term load forecasting. Firstly, the factors which may affect selecting similar days are analyzed in this paper. Then, in view of the characteristics of microgrid load, a novel method for selecting similar days is proposed in microgrid short-time load forecasting based on load point scale. The proposed method considers the cumulative effects of the weather factors, the continuity and periodicity of short-term load and the effects of time distance, and its evaluation function of day character similarity takes into account of day similarity and partial similarity and introduces the time factor. Thus, the proposed method overcomes the subjectivity of the traditional method which is based on personal experience. Case study demonstrates that the proposed method can improve the accuracy of short-term forecasting and can be applied in practice.

Key words: microgrid, short-time load forecasting, similar days, day character similarity, local shape similarity, time factor

中图分类号:

TM715

张玲玲，杨明玉，梁武. 微网用户短期负荷预测相似日选择算法[J]. 中国电力, 2015, 48(4): 156-160.

ZHANG Lingling, YANG Mingyu, LIANG Wu. Table 2 Estimation accuracy of three forecasting methods Method for Selecting Similar Days in Short-term Load Forecasting of Microgrid[J]. Electric Power, 2015, 48(4): 156-160.

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参考文献

[1] 李富生，李瑞生，周逢权．微电网技术及工程应用[M]．北京：中国电力出版社，2012．
[2] 尹昊．新能源微电网短期负荷预测[D]．长沙：湖南大学，2012．
[3] 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.
[4] 罗滇生，李伟伟，何洪英．基于局部形相似的超短期负荷预测方法[J]．电力系统及其自动化学报，2008，20（1）：75－79．
LUO Diansheng, HE Hongying. A shape similarity criterion based curve fitting algorithm and its application in ultra-short-term load forecasting [J]. Proceedings of the CSU-EPSA, 2008, 20(1): 75-79.
[5] 牛东晓，魏亚楠．基于FHNN相似日聚类自适应权重的短期电力负荷组合预测[J]．电力系统及其自动化，2013，37（3）：54－57．
NIU Dongxiao, WEI Yanan. Short-term power load combinatorial forecast adaptively weighted by FHNN similar-day clustering [J]. Automation of Electric Power Systems, 2013, 37(3): 54-57.
[6] 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]．电力系统及其自动化，2008，32（9）：69－73．
LI Canbing, LI Xiaohui, 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.
[7] 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 Xinbin, ZHANG Jun, ZHANG Yan, et al. Short-term load forecasting for similar days based on support vector machine and demister-shafer theory [J]. Power System Technology, 2010, 34(7): 143-147.
[8] 莫维仁，张伯明，孙宏斌，等. 短期负荷预测中相似日的探讨[J]. 清华大学学报：自然科学版，2004，44（1）：106－109．
MO Weiren, ZHANG Boming, SUN Hongbin, et al. Method to select similar days for short-term load forecasting[J]. Tsinghua University: Science & Technology, 2004, 44(1): 106-109.
[9] 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 Zhengling, TIAN Yong, ZHANG Guangtao, 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.
[10] 陈超，黄国勇，邵宗凯，等．基于日特征量相似日的PSO-SVM短期负荷预测[J]．中国电力，2013，46（7）：69－73．
CHEN Chao, HUANG Guoyong, SHAO Zongkai, et al. Short-term load forecasting for similar days based on PSO-SVM and daily feature vector [J]. Electric Power, 2013, 46(7): 69-73.
[11] 王海林，赵国刚，王颖，等．电力系统负荷分析及预测方法[J]．天津电力技术，2002（3）：13－15．
WANG Hailin, ZHAO Guogang, WANG Ying, et al. Analysis of power load and its forecasting[J]. Tianjin Electric Power, 2002, (3): 13-15.
[12] 刘晓娟，方建安. 基于双修正因子的模糊时间序列日最大负荷预测[J]. 中国电力，2013，46（10）：115－118．
LIU Xiaojuan, FANG Jianan. Maximum load forecasting based on a bi-factor revised fuzzy time series model[J]. Electric Power,2013, 46(10): 115-118.
[13] 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 Xianshi, GUO Hongzhen. Urban load saturation forecast based on ant cellular automata theory [J]. Electric Power,2011, 44(7): 17-20.
[14] 罗军，何光宇，张思远，等．基于负荷点相似的地区短期负荷预测新方法[J]．电网技术，2007，31（6）：67－71．
LUO Jun, HE Guangyu, ZHANG Siyuan, et al. A new method to forecast regional short-term load based on similar load point [J]. Power System Technology, 2007, 31(6): 67-71.
[15] 朱晓清，林健良，周鑫．基于负荷曲线距离和形状的负荷分类方法[C]// 2011年青年通信国际会议论文集. 2011年青年通信国际会议. 中国澳门，中国广东珠海，2011．
[16] 刘波．粒子群优化算法及其工程应用[M]．北京：电子工业出版社，2010．
[17] 张贲，史沛然，蒋超．气象因素对京津唐电网夏季负荷特性影响分析[J]．电力自动化设备，2013，33（12）：140－144．
ZHANG Ben, SHI Peiran, JIANG Chao. Impact of meteorological factors on summer load characteristics of Beijing-Tianjin-Tangshan power grid[J]. Electric Power Automation Equipment, 2013, 33(12): 140-144.