基于改进灰色模型的电力数据预测方法研究

Research on power data prediction method based on improved grey model

针对传统灰色模型在多原始数据、长时间尺度的负荷预测情景下预测精度差的问题,文中分析了灰色模型(Gray Model,GM)的基本原理,并提出相应的改进措施,其中包括原始数据的加权处理、选取合适的初始条件及自适应优化模型参数。并将改进灰色模型(Improved Grey Model,IGM)应用于电力负荷预测。通过算例分析结果表明,无论在短期负荷预测还是在中长期负荷预测的情景下,所提出基于改进灰色模型的电力负荷预测方法相比于传统灰色模型,均具有更高的预测准确性,能够为电力系统的安全、稳定运行以及合理的规划提供重要支撑。

In view of the poor prediction accuracy of the traditional grey model in the load forecasting scenario with multiple original data and long time scale,in this paper,the basic principle of gray model(GM)is analyzed,and the corresponding improvement measures are proposed,including the weighting of the original data,the selection of appropriate initial conditions and adaptive optimization model parameters.The improved grey model(IGM)is applied to power load forecasting.The results show that the improved grey model based power load forecasting method is more accurate than the traditional grey model,which can provide important support for the safe and stable operation of power system and reasonable planning.