电力负荷预测的方法很多,如统计预测方法,线形回归法、时间序列法和弹性系数法,直接建立数学表达式,对相关因素和负荷之间的关系加以描述;新兴的预测方法,如灰度预测方法,对负荷数据序列进行建模和分析。考虑到电力负荷受经济、社会、气候等不确定因素的影响,很难准确描述电力负荷预测的实际复杂变化规律。鉴于此,提出采用过程神经网络模型进行电力系统的短期负荷预 There are many methods of power load forecasting, such as statistical forecasting method, linear regression method, time series method and elastic coefficient method, directly establishing mathematical expressions and describing the relationship between relevant factors and load. Emerging forecasting methods such as grayscale Forecasting methods to model and analyze load data sequences. Taking into account the power load by the economic, social, climate and other uncertainties, it is difficult to accurately describe the actual power load forecasting the law of complex changes. In view of this, proposed using the process neural network model for short-term load forecasting of power system