基于指数加权移动平均多维组合模型的电力负荷预测

Power Load Forecasting Based on Multidimensional Combined Model of Exponential Weighted Moving Average

随着电力行业的不断发展,对电力用户侧进行用电负荷预测成了满足用户用电供需平衡和电网规划的重要部分。在大数据背景下,为提高电力负荷预测结果的准确性,针对历史数据时间远近的影响,分别考虑同期历史数据和近期历史数据两类数据局限性的影响,基于时间占优的原理,引入指数加权移动平均模型对不同时刻的数据进行权重分配,提出了改进的电力负荷预测模型。以某地区电力负荷预测为例,所得预测结果在标准误差上提高了29.5%,平均绝对百分误差提高了25.7%,分析结果表明提出的模型是可行的且有较高的精确度,为电力负荷的预测提供可靠的参考依据。

With the continuous development of the power industry, the prediction of power load on the power user side has played an important part in meeting the balance between power supply and demand of users and power grid planning. Under the background of big data, in order to improve the accuracy of power load forecasting results, and in view of the influence of the time for historical data, considering the limitations in historical data of the same period and recent historical data respectively, based on the principle of time-dominant, the exponentially weighted moving-average was introduced to distribute the weight of data at different time, and an improved power load forecasting model was proposed. Taking the power load forecasting in a certain area as an example, the predicted result was improved by 29.5% in root mean squared error and the mean absolute percent error increased by 25.7%. The analysis results show that the proposed model is feasible and has high accuracy, providing a reliable reference for the power load forecasting.