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.
Highlights
IntroductionThe power load sequence contains relatively obvious white noise
2.2 Kalman filtering based on time series model Kalman filtering method, a kind of effective recursive filtering method, estimates the system state according to a series of measurements including stochastic noise
According to the auto regressive moving average (ARMA) model, Kalman filtering method is adopted to suppress the stochastic noise of power load
Summary
The power load sequence contains relatively obvious white noise. The prediction accuracy of power load is related to the length of historical observation data. With noise and chaos in the observed data, different time series have different upper limit of prediction accuracy [3, 4]. It is important to estimate the noise intensity directly from the observed data and to separate the noise from the observed data, which is very important to improve the accuracy of the power load forecasting result. To improve the quality of power load data, stochastic noise present in the load data must be identified and filtered out [5, 6]. There are mainly following methods in the power load forecasting field, such as regression analysis, combined forecasting, exponentially smoothing, neural network and wavelet methods, and so on. Time series method and Kalman filter algorithm are proposed to filter the power load stochastic noise by pretreating and statistically testing of power load data, the total variance method is used to evaluate the stochastic errors of the load data before and after filtering effectively
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