Combination Forecasting of Power Load Based on Polynomial Trend Extrapolation and ARIMA Model

Abstract:

The power load forecasting is the core component of the early warning system for fuel storage margin in power system and an important guarantee to the early warning function to achieve. In this paper, one province's 2008 load data is chosen to forecast the electricity consumption in 2009. Firstly the two forecasting models of polynomial trend extrapolation and ARIMA are established, and then the combined model of them is used to forecast, that is, the final result is equal to the sum of the trend value by polynomial extrapolation and the non-trend D-value’s forecasting result by ARIMA. The results indicate that the combination forecasting make the forecast accuracy significantly improved and ensure the effective operation of the early warning system.

• Periodical: Advanced Materials Research (Volumes 546-547)
• Pages: 357-362
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.546-547.357
• Online since: July 2012
• Authors: Jing Jing Xia, Huan Qi, Zi Qi Wang
• Keywords: Arima, Combination Forecasting of Power Load, Early Warning System, Polynomial Trend Extrapolation

References

[1] Chongqing Kang, Qing Xia, and Mei Liu, Power System Load Forecasting, Beijing: China Electric Power Press, (2007).

[2] Hong Xie, Zhonghao Cheng, Guoli Zhang, and Dongxiao Niu, Linear sparse AR forecasting model of load forecasting, Learned journal of North China Electric Power University, Forum Vol. 31, No. 1(Jan. 2004), pp.30-32.

[3] Zudi Lu, The compare between correlation structure of double timing AR-MA model and ARMA model, Systems Science and Mathematics, Forum Vol. 15, No. 3(Jul. 1995), pp.222-230.

[4] Guijun Ye, Yaohua Luo, Yong Liu, and Hongzhang Jin, Research of load forecasting methods in power system based on ARMA model, Information technology, No. 6(2002), pp.74-76.

[5] Liang Chang, Analyzes and forecasts of ARIMA model based on time series analysis, Computer age, No. 2(2011), pp.8-10.

[6] Dehong An, Xiangyue Liu, Jiakun Liu, and Shujin Xu, Modeling and forecasting in power load based on seasonal ARIMA model, Learned journal of Tianjin University, Forum Vol. 37, No. 2(Feb. 2004), pp.184-187.

[7] Ya Liu, Guozhong Zhang, and Fei He, Power load forecasting based on ARIMA model and BP network, Hubei Electric Power, Forum Vol. 27, No. 2(Apr. 2003), pp.13-15.

[8] Jinmu Yan, Power load forecasting based on method of trend extrapolation, Chinese foreign investment, No. 4(Feb. 2011), p.228.

[9] Ying Guo, Yaming Dang, and Xiushan Lu, Improved SISE orecasting model based on trend extrapolation of time series, Science of Surveying and Mapping, Forum Vol. 32, No. 1, Jan. 2007, pp.34-35, 52.

[10] Jun Han and Yaming Liang, Combination forecasting in energy demand based on trend extrapolation and ARMA, Learned journal of Lanzhou Commercial College, Forum Vol. 21, No. 6(Dec. 2005), pp.92-95.

[11] Qin Si, Application of the combination model based on trend extrapolation and ARMA in energy consumption, Learned journal of Inner Mongolia College of Finance and Economics, No. 2(2010), pp.43-4.