基于扩大周期的电力负荷预测模型

Power Load Forecasting Model Based on Expansion Period

针对现有电力负荷预测模型依赖近期数据导致预测结果偏离时间序列真实情况的问题,文中提出了基于扩大周期信息的电力负荷预测模型。将预处理完的电力负荷时间序列按照同一时刻不同天进行处理,在此基础上分别利用ARIMA(Autoregressive Integrated Moving Average Model)模型和LSTM(Long Short-Term Memory Network)模型进行建模分析,并采用3种评价指标评估模型的预测表现。预测结果表明,扩大周期信息构建的ARIMA模型的3种评价指标都比传统ARIMA模型低,对应的RMSE(Root Mean Square Error)、MAE(Mean Absolute Error)和MAPE(Mean Absolute Percentage Error)分别为32 434.114 8、5 828.390 9和0.025 2;扩大周期信息的LSTM模型也比原始LSTM模型低,对应的RMSE、MAE和MAPE分别为13 520.497 4、9 298.352 6和0.091 4。

In view of the problem that the existing power load forecasting models rely on recent data,which leads to the prediction results deviating from the real situation of the time series,a power load forecasting model based on extended period information is proposed.The pre-processed power load time series is processed according to the same time and different days.On this basis,the ARIMA(Autoregressive Integrated Maving Average Model)model and LSTM(Long Short-Term Memory Network)model are used for modeling and analysis,and three evaluation indicators are used to evaluate the predictive performance of the model.The prediction results show that the three evaluation indexes of the ARIMA model constructed by expanding the period information are lower than those of the traditional ARIMA model,and the corresponding RMSE(Root Mean Square Error),MAE(Mean Absolute Error)and MAPE(Mean Absolute Percentage Error)are 32434.1148,5828.3909 and 0.0252,respectively.The LSTM model of expanding the period information is also lower than the original LSTM model,and the corresponding RMSE,MAE,and MAPE are 13520.4974,9298.3526,and 0.0914,respectively.