基于时间序列的电力负荷数据分析

Analysis of Electric Load Data Based on Time Series

时间序列分析方法是电力负荷预测领域的重要工具之一,它主要通过建立相关模型描述历史数据随时间动态变化的规律以预测未来的值。本文采用温特线性与指数平滑法和季节乘积ARIMA模型对电力负荷实测数据进行建模,然后分别使用平均相对误差MAPE衡量预测精度。研究结果表明:两种方法均有较高的拟合与预测精度。

Time series analysis method is one of the important tools in the field of power load forecasting. It mainly describes the law of the historical data dynamic change over time to predict the future value by establishing a relevant model. In this paper, Winter’s exponential smoothing method and seasonal ARIMA model are applied to model estimating on the power load data, and the authors use the Mean Absolute Percentage Error (MAPE) to measure the accuracy. The results prove that both of them have high fitting and forecasting precision.