摘要:电力负荷预测是电力生产、调度部门的重要工作之一，它的实质是根据预测对象的历史数据建立相应的数学模型，描述其发展规律。因此，能否对电力负荷进行可靠预测直接关系到电力行业的经济效益。

　　　本文采用了基于灰色理论与BP神经网络相结合的电力负荷预测方法。文中首先介绍了电力负荷预测的原理、起源以及当前国内外电力负荷预测领域的概况，同时介绍了目前常用的几种电力负荷预测方法，并对新引入电力系统的灰色预测理论及其相应的各种模型的基本原理做了简要的探讨。然后用三种不同模型（灰色GM(1,1)模型，一元线性回归模型，等权平均组合模型）进行预测，然后把预测值作为训练样本对神经网络进行训练，并用训练好的网络进行负荷预测。由于该预测模型采用了灰色理论与BP神经网络相结合的预测算法，吸取了二者的优点，避免了单一预测模型所存在的预测风险。

　　　从对重庆市某供电局2007各月电量负荷预测实例中可以看到，较灰色单一预测模型而言，该方法的预测精确度和稳定性都有明显的改进，理论上是可行的，并对电力行业进行计划拟定和生产运行均具有一定的指导和应用意义。

关键词：灰色理论；BP神经网络；负荷预测；精度

Abstract：Power load forecasting power production, scheduling department is one of the most important work, its essence is according to the historical data of the forecasted object establish corresponding mathematical model, described the law of development. So, can make to the electric power load forecasting for reliable directly related to the economic benefits of electric power industry. 

    In this paper based on the Grey theory and the BP neural network of power load forecasting method, this paper firstly introduces the principle of power load forecast, the origin and current domestic and international power load forecasting the general situation of the field, and introduced the common several power load forecasting method, and the introduction of new power system Grey prediction theory and its corresponding all sorts of model of the basic principles do was briefly discussed in this paper. And then in three different model (Grey GM (1, 1) model, the linear regression model, a right to such as average combination model) to carry on the forecast, and then the predicted as the training sample the neural network training, and training good network load forecasting. Because of the prediction model using the Grey system theory and the BP neural network with the combination of prediction algorithm, drawing on the strengths, to avoid the forecast model of the existing prediction risk.  

    In Chongqing from a power supply bureau was produced 2007 power load forecasting example can see, a Grey forecast model in the prediction accuracy and stability of the method are the obvious improvement, this algorithm in theory and practice is feasible, and the electric power industry for planning and production operation has certain directive and all applications.

Keywords: Grey theory; The BP neural network; Load forecasting; Precision