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基于SARIMA-GS-SVR组合模型的短期电力需求预测 被引量:2

Short-Term Power Demand Forecasting Based on SARIMA-GS-SVR Combined Model
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摘要 短期电力需求预测在合理分配电力利用、减少能源浪费和增强电力系统的并网运行方面具有重要作用。应用单一的季节自回归移动平均模型对电力需求预测将限制预测精度。为了提高SARIMA的预测精度,文中提出了SARIMA-GS-SVR组合预测模型。采用网格搜索算法将SARIMA预测的残差带入支持向量回归模型进行参数训练,并将寻优的最佳参数带入SVR对残差进行预测。将得到的残差预测结果和SARIMA预测结果加和进行综合分析。建立SARIMA、SVR、GS-SVR和SARIMA-GS-SVR预测模型,以加利福尼亚州电力需求历史数据为例,对该地某日24 h的电力需求进行预测。为了体现模型整体的优越性,选用指数平滑法作为无关基准模型进行实验对比。实验结果表明,相比SARIMA,SARIMA-GS-SVR的预测精度提高了29.1812%,且其MAE、MAPE和RMSE3种误差指标评价值低于其它4种模型。 Short-term power demand forecasting plays an important role in the rational distribution of power utilization,reducing energy waste and enhancing the grid-connected operation of the power system.Using the single model of the seasonal auto regressive integrated moving average to forecast electricity demand will limit its prediction accuracy.In order to improve the prediction accuracy of the SARIMA model,the SARIMA-GS-SVR combined forecasting model is proposed in this study.The grid search algorithm is used to bring the residual predicted by SARIMA into the support vector regression model for parameter training,and the best parameters for optimization are brought into the SVR to predict the residuals.The obtained residual prediction results and the SARIMA prediction results are added together for comprehensive analysis.SARIMA,SVR,GS-SVR and SARIMA-GS-SVR forecasting models are established,and California’s historical electricity demand data is taken as an example to predict the 24-hour electricity demand in California on a certain day.In order to reflect the overall superiority of the model,the exponential smoothing method is selected as an irrelevant benchmark model for experimental comparison.The results show that compared with the SARIMA model,the prediction accuracy of the SARIMA-GS-SVR model is increased by 29.1812%,and the three error index values of the SARIMA-GS-SVR model such as MAE,MAPE and RMSE are significantly lower than the other four models.
作者 刘晗 王万雄 LIU Han;WANG Wanxiong(College of Sciences,Gansu Agricultural University,Lanzhou 730070,China)
出处 《电子科技》 2022年第8期58-65,共8页 Electronic Science and Technology
基金 国家自然科学基金(1197124)。
关键词 电力需求预测 残差预测 预测精度 季节差分自回归移动平均 网格搜索算法 支持向量回归 指数平滑法 参数寻优 electricity demand forecasting residual prediction prediction accuracy seasonal auto regressive integrated moving average grid search algorithm support vector regression exponential smoothing method parameter optimization
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