Abstract
Accurate and efficient ultra-short-term load forecasting is crucial for industrial power users to have stabilized and optimized operations. In this paper, we develop novel strategies for industrial power users to handle their challenges in ultra-short-term load forecasting. Firstly, this paper proposes a two-way Genetic Algorithm Back Propagation Neural Networks (GABPNN) missing data completion model to handle data loss, which is common power load data mining. A particle swarm optimization - supporting vector regression (PSO-SVR) algorithm is further used to integrate the two-way completion results with better accuracy. In addition, the paper introduces a combined ultra-short-term load forecasting model for industrial power users. The proposed model combines the Cubature Kalman filter (CKF) prediction model with good performance in nonlinear dynamic systems and the least square support vector machine (LS-SVM) prediction model with good performance in small-scale data prediction. The grey neural network is used to integrate the two algorithms, which further improves the accuracy of ultra-short-term load forecasting. Lastly, we test the proposed strategy in case study with real industrial data and demonstrate that the proposed model has a high degree of precision in load forecasting.
Highlights
As the foundation of national life, the stability of power system determines the progress of science and technology and economic development
In order to effectively improve the problems of the above methods, this paper adopts a supporting vector regression (SVR) algorithm based on particle swarm optimization [22]
The ultra-short-term load forecasting will use the gray neural network to realize the fusion of the least squares support vector machine model and the Cubature Kalman filter model, and realize the combination of the horizontal prediction and the longitudinal prediction of the prediction model, and further improve the prediction through more reasonable weight distribution
Summary
As the foundation of national life, the stability of power system determines the progress of science and technology and economic development. LOAD DATA COMPLETION MODEL BASED ON IMPROVED TWO-WAY GABPNN In order to solve the problem of data loss in power system load forecasting, this paper avoids the possible deviations in the data mining process such as ultra-short-term load forecasting due to the problem of load data integrity and validity [17]. An online completion method for power load missing data based on improved two-way GABPNN neural network is proposed. The data training model complements the missing data points in two directions and introduces the PSOSVR algorithm to achieve the fusion of the calculation results in two directions, replacing the traditional weighted summation method, so that the data change trend after the deletion is considered to ensure the accuracy of data completion. In order to improve these problems, this paper chooses a genetic algorithm combined with BP neural network to complete the missing load data
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