Impact of Smart Grid Technology on Power Distribution Efficiency
DOI:
https://doi.org/10.52152/4171Keywords:
Distribution Efficiency, Particle Swarm Optimization, Smart Grid, Support Vector Regression, Load PredictionAbstract
Traditional smart grids lack real-time data analysis capabilities when processing large-scale data, resulting in insufficient timeliness of power dispatching and fault response. Low load forecasting accuracy affects the accuracy of distribution dispatching and reduces distribution efficiency. This paper proposed a solution that combined PSO (Particle Swarm Optimization) and SVR (Support Vector Regression). PSO optimizes power resource dispatching and minimizes energy consumption under constraints by adjusting the operating status of equipment. SVR predicts future loads through regression analysis of historical load data to provide accurate support for distribution planning. The paper applied the PSO to hyperparameter and penalty factors to improve prediction accuracy. According to the PSO optimization and SVR prediction results, the power grid dispatching plan is adjusted in real time, and the power flow and equipment load are dynamically adjusted to ensure the smooth operation of the system under different loads and improve dispatching efficiency. The experimental results show that the average MSE (Mean Square Error) of SVR under different samples is 0.30, and the average MAE (Mean Absolute Error) is 0.45, with high prediction accuracy. After PSO optimization, the energy saving rate and load balancing rate of the dispatching system under high load conditions increased by 5% and 10% respectively, the dispatching time was shortened by 15 seconds, and the fault response time was shortened by 15 seconds, with higher dispatching efficiency and real-time performance.
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Copyright (c) 2025 Dong Lv, Yuanqing Pan, Yijie Xia, Xiaolin Liu , Lingjie Wu (Author)
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