Time series modeling and filtering method of electric power load stochastic noise

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.

Coordinated active and reactive power optimization considering load characteristics for active distribution network

“…(1) and (2), the active and reactive power of the voltage-sensitive load in Eqs. 3and (4) are only proportional to the voltage, which has a linear relationship.…”

“…Sensitive loads require the voltage to be as stable as possible or fluctuate very little. When the voltage fluctuates, the sensitive load will fail and lead to the imbalance of the entire power system [4] . Therefore, the stability of sensitive loads and active distribution network (ADN) should be guaranteed to minimize the cost of power system.…”

Real-Time Identification of False Data Injection Attacks: A Novel Dynamic-Static Parallel State Estimation Based Mechanism

“…Considering the time continuity of the system dynamics, in this paper, auto regression moving average (ARMA) model with proper order is employed to represent the dynamic transfer model of the system [37]. Besides, due to the dynamic process of the system is full of fluctuation, it is not reasonable to consider the system process noise q k and Q k as constants.…”

A Reliable Short‐Term Power Load Forecasting Method Based on VMD‐IWOA‐LSTM Algorithm

“…Kalman filtering and auto regressive a Correspondence to: Tao Jin. E-mail: jintly@fzu.edu.cn moving average model are used in [6] to significantly reduce the random noise in ultra-short-term power load forecast. Empirical mode decomposition (EMD) [7], singular spectrum analysis [8] are applied on LSTM model respectively, to train the modes extracted from load curves and observe irregular noise characteristics.…”

“…In addition to the traditional multiple regression analysis, back propagation neural network (BPNN) [2], long short‐term memory (LSTM) [3], deep residual network [4], similar day method [5] and other methods continue to be applied, more researchers try to combine them with different data processing methods. Kalman filtering and auto regressive moving average model are used in [6] to significantly reduce the random noise in ultra‐short‐term power load forecast. Empirical mode decomposition (EMD) [7], singular spectrum analysis [8] are applied on LSTM model respectively, to train the modes extracted from load curves and observe irregular noise characteristics.…”