Power Grid Saturation Load Probability Prediction Method Considering Random Influencing Factors

doi:10.11985/2021.03.014

Abstract

Abstract:

In order to effectively solve the problems such as the accuracy of current research results of power grid load forecasting to be optimized, a probability forecasting method of power grid saturated load based on particle swarm optimization algorithm is proposed. Considering the influence factors of the saturated load of the power grid, the K-means clustering method is used to divide the power supply area. Based on the division results, the Gaussion process regression model and particle swarm optimization method are introduced to obtain the prediction value of the remaining historical information data of the power grid by the rolling prediction method. At the same time, the particle swarm optimization method is used to solve the optimization problem to obtain the optimal hyper parameters and determine the saturation of the power grid. According to the combination with the random factors of saturated load, the probability prediction model of saturated load is constructed. The experimental results show that the proposed method has high non-standard fitting index and comparative fitting index, strong fusion saturation criterion and reliability.

Key words: Influencing factor, grid saturation load, probability prediction model

CLC Number:

TM715

JING Linguo, JING Zhongyi, ZHANG Shaojing, ZHANG Shaoying. Power Grid Saturation Load Probability Prediction Method Considering Random Influencing Factors[J]. Journal of Electrical Engineering, 2021, 16(3): 99-105.

Figures/Tables 5

References 15

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方法	电网饱和区间/(×10⁸ kW·h)	饱和期望值 /(×10⁸kW·h)	随机影响因素规划数值	最终饱和规划数值	鲁棒性(%)
本文方法	152.1~200.6	200.6	4.1	196.5	98
文献[4]	147.5~196.1	158.7	7.4	151.3	95.4
文献[5]	165.7~189.4	185.5	10.6	174.9	94.3
文献[6]	132.3~167.1	165.6	5.7	150.9	91.5