| Citation:Li Huang,Yongbiao Yang,Honglei Zhao,Xudong Wang,Hongjuan Zheng.Time series modeling and filtering methodof electric power load stochastic noise[J].Protection and Control of Modern Power Systems,2017,V2(3):269-275[Copy] |
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| Abstract: |
| Stochastic noises have a great adverse effect on the prediction accuracy of electric power load. Modeling online
and filtering real-time can effectively improve measurement accuracy. Firstly, pretreating and inspecting statistically
the electric power load data is essential to characterize the stochastic noise of electric power load. Then, set order
for the time series model by Akaike information criterion (AIC) rule and acquire model coefficients to establish
ARMA (2,1) model. Next, test the applicability of the established model. Finally, Kalman filter is adopted to process
the electric power load data. Simulation results of total variance demonstrate that stochastic noise is obviously
decreased after Kalman filtering based on ARMA (2,1) model. Besides, variance is reduced by two orders, and every
coefficient of stochastic noise is reduced by one order. The filter method based on time series model does reduce
stochastic noise of electric power load, and increase measurement accuracy. |
| Key words: Electric power load, Stochastic noise, ARMA model, Kalman filter |
| DOI：10.1186/s41601-017-0059-8 |
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| Fund: |
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