Due to the diversity of data variation, the fluctuations characteristic of the medium-term power load is different from short-term and long-term power load. Based on the view of electric power system complexity, the mid-term power load forecasting problem is discussed, including the forecasting model uncertainty, parameter time-varying characteristics and the periodic law of load fluctuation. According to the features of mid-term power load, a semi-parametric model based on nonparametric smoothing is built, and the division of the function interval is defined. After that, a new dynamic prediction method is put forward based on variable interval. Combined with the ensemble empirical mode decomposition algorithm and wave energy test, the noise sequence analysis and separation method is presented. The study shows that, climatic factor have the greatest impact on the electricity consumption, while economic factor has less impact on it. In different forecast periods, the explanatory of factors to the forecasting model varies over time. As the proposed semi-parametric model can be used for accurate multi-dimensional and multi-granularity analysis of electricity load, then grasp the variation, it can be efficiently used for mid-term load forecasting.
SHAO Zhen, YANG Shan-lin, GAO Fei, WANG Xiao-jia
. Mid-term load Forecasting Semi-Parametric Model Based on Time-variant Interval Weights[J]. Chinese Journal of Management Science, 2015
, 23(3)
: 123
-129
.
DOI: 10.16381/j.cnki.issn1003-207x.2015.03.015
[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.
[2] McSharry P E, Bouwman S, Bloemhof G. Probabilistic forecast of the magnitude and timing of peak electricity demand[J]. IEEE Transactions on Power Systems, 2005,20(2):1166-1172.
[3] 王晓佳,杨善林.基于组合差值的GM(1,1)模型预测方法的改进与应用[J].中国管理科学,2012,20(2):129-134.
[4] 史会峰,牛东晓,卢艳霞.基于贝叶斯神经网络短期负荷预测模型[J].中国管理科学,2012,20(4):118-124.
[5] Hippert H S,Pedreira C E,Souza R C.Neural networks for short-term load forecasting: a review and evaluation[J]. IEEE Transactions on Power Systems,2001,16(1):44-55.
[6] 张宜阳,卢继平,孟洋洋,等.基于经验模式分解和混沌相空间重构的风电功率短期预测[J].电力系统自动化,2012,36(5):24-28.
[7] 李瑾,刘金朋,王建军.采用支持向量机和模拟退火算法的中长期负荷预测方法[J].中国电机工程学报,2011,31(16):63-66.
[8] 邰能灵,侯志俭,李涛,等.基于小波分析的电力系统短期负荷预测方法[J].中国电机工程学报,2003,23(1):45-50.
[9] Ruppert D, Wand M P, Carroll R J.Semiparametric regression[M].Cambridge:Cambridge University Press,2003.
[10] Hamilton J D. Time series analysis[M].Princeton:Priceton University Press,1994.
[11] Engle R F, Granger C W J, Rice J. Semi-parametric estimates of the relation between weather and electricity sales[J].Journal of the American Statistical Association,1986,81(394):310-320.
[12] Charytoniuk W, Chen M S, Van Olinda P. Nonparametric regression based short-term load forecasting[J]. IEEE Transac-tions on Power Systems,1998,13(3):725-730.
[13] Baccini M, Biggeri A, Lagazio C. Parametric and semi-parametric approaches in the analysis of short-term effects of air pollution on health[J].Computational Statistics & Data Analysis,2007,51(9):4324-4336.
[14] Fan Shu, Hyndman R J.Short-term load forecasting based on a semi-parametric additive model[J]. IEEE Transac-tions on Power Systems,2012,27(1):134-141.
[15] Stone C J.Consistent nonparametric regression[J].Applied Statistical Society 1977,5(4):595-635.
[16] Heckman N E. Spline smoothing in partial linear models[J].Journal of the Royal Statistical Society, Series B,1986,48(2):244-258.
[17] Green P J.On Use of the EM for penalized likelihood estimation[J].Journal of the Royal Statistical Society, Series B(Methodological),1990,52(3):443-452.
[18] Huang N E,Shen Zheng,Long S R.The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J].Proceedings of the Royal Society A, 1998,454(1971):903-995.
[19] Wu Zhaohua, Huang N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis,2009,1(1):1-41.
