SG  >> Vol. 5 No. 4 (August 2015)

    电力系统短期负荷的WGBR预测方法研究
    Wavelet Gradient Boosting Regression Method Study in Short-Term Load Forecasting

  • 全文下载: PDF(826KB) HTML   XML   PP.189-196   DOI: 10.12677/SG.2015.54023  
  • 下载量: 1,719  浏览量: 5,132   国家自然科学基金支持

作者:  

王 虹,谷根代:华北电力大学数理学院,河北 保定

关键词:
电力系统短期负荷预测小波变换梯度Boosting回归Electrical Power System Short-Term Load Forecasting Wavelet Transform Gradient Boosting Regression

摘要:

考虑到气象因素对电力负荷的影响,并结合负荷和气象数据的特点,本文提出了基于小波变换的梯度Boosting回归(Gradient Boosting Regression based on Wavelet transform, WGBR)预测方法。该方法将负荷和气象数据分别分解为不同频段的小波子序列,对各子序列分别利用GBR算法建模预测,最后综合得到负荷序列预测结果。经华北地区某市的负荷数据验证得本文方法取得了较好的预测精度。

The authors proposed gradient boosting regression method based on wavelet transform consi-dering the influence of weather factors and the characteristics of the load and meteorological data. The load and meteorological data were decomposed into several subsequences in different band by wavelet transform respectively. Forecasting the load subsequence by building different gradient boosting regression model, lastly, the final forecasting result is attained via adding all child-load-serials forecasting results. It has been showed by load data of a city in north China that the method achieved good prediction accuracy.

文章引用:
王虹, 谷根代. 电力系统短期负荷的WGBR预测方法研究[J]. 智能电网, 2015, 5(4): 189-196. http://dx.doi.org/10.12677/SG.2015.54023

参考文献

[1] 葛少云, 贾鸥莎, 刘洪 (2012) 基于遗传灰色神经网络模型的实时电价条件下短期电力负荷预测. 电网技术, 1, 224-229.
[2] Prakash, A. and Singh, S.K. (2014) Towards an efficient regression model for solar energy prediction. 2014 Innovative Applications of Computational Intelligence on Power Energy and Controls with Their Impact on Hu-manity (CIPECH), 18-23.
http://dx.doi.org/10.1109/CIPECH.2014.7019040
[3] Guelman, L. (2012) Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications, 39, 3659-3667.
http://dx.doi.org/10.1016/j.eswa.2011.09.058
[4] 鲁庆, 穆志纯 (2014) 应用提升回归树研究碳钢的土壤腐蚀规律. 中南大学学报(自然科学版), 6, 1879-1886.
[5] 高云龙, 潘金艳, 吉国力, 高峰 (2011) 基于Boosting梯度下降理论的时间序列建模方法. 中国科学: 技术科学, 7, 929-943.
[6] 乐斌 (2004) Boosting算法研究及其在光谱分析中的应用. 硕士论文, 浙江大学, 杭州.
[7] 李航 (2012) 统计学习方法. 清华大学出版社, 北京.
[8] 吴喜之, 马景义, 吕晓玲, 闫洁 (2009) 数据挖掘前沿问题. 中国统计出版社, 北京.
[9] Friedman, J.H. (2001) Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 5, 1189- 1232.
http://dx.doi.org/10.1214/aos/1013203451
[10] 徐科, 徐金梧, 班晓娟 (2001) 基于小波分解的某些非平稳时间序列预测方法. 电子学报, 29, 566-569.
[11] Hastie, T., Tibshirani, R. and Friedman, J. (2009) Elements of statis-tical learning: Data mining inference, and prediction. Springer-Verlag New York Inc., New York.
http://dx.doi.org/10.1007/978-0-387-84858-7
[12] 王兴玲, 李占斌 (2005) 基于网格搜索的支持向量机核函数参数的确定. 中国海洋大学学报(自然科学版), 35, 859-862.