基于优化权重的卡尔曼滤波与无偏灰色组合模型的短期负荷预测

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Abstract According to the dynamic and stochastic characteristics of short-term load forecasting, a Kalman filtering and unbiased grey combination forecasting model is proposed. The model gives full play to the advantage of the Kalman filter to estimate the dynamic system, and makes use of the unbiased grey model to explore the characteristics of the random data. According to the defects of the special day convergence in the Kalman filter prediction, this paper makes use of the unbiased grey theory which is stable, regular and eliminates the inherent deviation. According to the trend that the trend of the unbiased grey theory is stable and the error of some data points in the short-term load forecasting is large, the Kalman filter is used to compensate the defect. Linear combination method is used to further avoid the prediction risk. The results show that the model has high precision and practicability.

Key words： Kalman filtering model unbiased grey model short term load forecasting combination forecasting

Published: 20 September 2017

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	Articles by authors
	Lin Tianxiang
	Zhang Ning
	Hu Jun

Cite this article:

Lin Tianxiang,Zhang Ning,Hu Jun. Short Term Load Forecasting based on Kalman Filter and Unbiased Grey Combination Model[J]. Electrical Engineering, 2017, 18(9): 19-23.

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http://dqjs.cesmedia.cn/EN/Y2017/V18/I9/19

[1] Rahman S, Bhatnagar R. An expert system based algorithm for short-term load forecast[J]. IEEE Transactions on Power Systems, 1988, 3(2): 392-399.
[2] Ho K L, Hsu Y Y, Chen C F, et al. Short term load forecasting of Taiwan power system using a Know-ledgebased expert system[J]. IEEE Trans on Power Systems, 1990, 5(4): 1214-1221.
[3] 陈亚, 李萍. 基于神经网络的短期电力负荷预测仿真研究[J]. 电气技术, 2017, 18(1): 26-29.
[4] Haykin S. Neural networks: AComprehensive founda- tion[M]. NewYork: Macmillan College Publishing Company, 1994.
[5] Mastorocostas P A, Theocharis J B, Bakirtzis A G. Fuzzy modeling for short term load forecasting using the orthogonal least squares method[J]. IEEE Trans on Power Systems, 1999, 14(1): 29-36.
[6] Song K B, Baek Y S, Hong D H, et al. Short-term load forecasting for the holidays using fuzzy linear regression method[J]. IEEE Transactions on Power Systems, 2005, 20(1): 96-101.
[7] 于龙, 郑益慧, 王昕, 等. 基于SVM与相似日的短期电力负荷预测[J]. 电工技术学报, 2013, 28(1): 217-223.
[8] 水乃翔, 秦禹春. 关于灰色系统GM(1,1)模型的一些理论问题[J]. 系统工程理论与实践, 1998(4): 59-63.
[9] 穆勇. 无偏灰色GM(1,1)模型的直接建模法[J]. 系统工程与电子技术, 2003(9): 53-54.
[10] Ngan H W, Fung Y F, et al. An Advanced Evolutionary Algorithm for Load Forecasting with the Kalman Filter[A]. IEEE Conference Publication. HongKong: 2001. 134-138.
[11] 李明干, 孙健利, 刘沛. 基于卡尔曼滤波的电力系统短期负荷预测[J]. 继电器, 2004, 32(4): 9-12.
[12] Al-Hamadi H M. Soliman S A.short-term electric load forecastingbased on kalman filtering algorithm with moving Windows weatherand load model[J]. Electric Power Systems Research, 2004(68): 47-59.
[13] 邓自立. 卡尔曼滤波与维纳滤波-现代时间序列分析方法(Kalman Filter and Wiener Filter-Modern Time Serials Analysis)[M]. 哈尔滨: 哈尔滨工业大学出版社, 2001.
[14] 李林川, 吕冬, 武文杰. 一种简化的电力系统负荷线性组合预测法[J]. 电网技术, 2002, 26(10): 10-13.
[15] 张亚军, 刘志刚, 张大波. 一种基于多神经网络的组合负荷预测模型[J]. 电网技术, 2006, 30(21): 21-25.