摘要
基于灰色系统的理论研究方向,建立智能电网短期负荷预测模型,将负荷预测的关键性评价指标——平均绝对百分误差(MAPE)通过采用粒子群优化PSO计算方法,通过优选实验模型中的阶数和背景参数实现预测和实际运行曲线逐步靠近甚至重合的结果。为达到模型预测的高精度,再通过运用"马尔科夫过程"进一步对原先预测的残值数差进行修正,实现智能电网中短期负荷预测。
On the basis of grey theory, a electrical load model was established to forecast the short-term electrical load. Using minimum MAPE as the evaluation index, the optimization of background value and model order in the model is realized by PSO algorithm to yield a conclusion that the forecast curve is generally similar to the real curve. To further improve the accuracy of model, the residual error state is forecast by Markov process to correct the residual error.
作者
王妍
王雪飞
Wang Yan;Wang Xuefei(College of Electrical and Automatic Engineering,Nanjing Normal University,Nanjing,210042,China;Nanjing Power Supply Company,210019,China)
出处
《仪器仪表用户》
2019年第6期17-19,71,共4页
Instrumentation
