Multi feature short term load forecasting based on VMD Stacking ensemble learning

DOI:	10.11809/bqzbgcxb2024.02.028
Keywords:	short term power load forecasting; variational modal decomposition; Stacking ensemble learning; multi feature variables; light gradient boosting machine; extreme gradient boosting
Abstract:	Aiming at the problem that the accuracy of current short term power load forecasting results is not high enough, a multi feature variable short term load forecasting model is proposed, which is composed of variational modal decomposition (VMD) and Stacking ensemble learning framework. Before forecasting, the VMD algorithm is used to decompose the load data, and then feature variables that are of high importance to the model are added. Then, the Stacking ensemble learning forecasting model, which is composed with the light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost), and the impact of different weather conditions on the accuracy of the forecasting model is compared. The comparison of actual examples shows that the error of multi feature VMD Stacking ensemble learning prediction model is small. Using VMD algorithm to decompose historical load series, the periodicity of sub modal components after decomposition is reflected, making it easier for the model to predict loads with high volatility; The key factors affecting load changes (such as temperature, weather, lunar calendar, and holiday conditions) have been taken into account, and the accuracy of the model has been improved; Stacking ensemble learning model learns from each other’s strong points to complement each other’s weak points, and its generalization ability is enhanced. Its prediction accuracy is higher than that of a single model.
Issue:	Vol. 45 No. 2 (2024)
Published:	2024-02-28
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