Abstract: The time series of energy load has a dual trend of periodic fluctuation and growth. By integrating multiple classification and regression trees（CART）， the existing gradient boosting tree（GBDT） can well fit the periodic fluctuation trend， but it is poor for the growth trend. The related researches decompose the load first， and then use the combined model for prediction. This paper studies the prediction using multi-component ensemble learning after decomposition. Firstly， the two trend components and residual components of load are decomposed， and the leaf node of CART is changed into three prediction models， so that it can predict the three components. At the same time， the CART loss function is optimized as the sum of the square errors of the prediction results of each component， so that it can consider the loss of the three component prediction models. Then， the prediction results are reconstructed based on gradient boosting， so that it can fit the dual trend of load in the way of multi-component ensemble learning. Finally， a load forecasting method based on decomposition and multi-component ensemble learning is proposed. In a regional power load forecasting experiment， compared with other forecasting methods， the error evaluation values of the proposed method are reduced. The results of experiments show that in the prediction of double trend load， the method proposed in this paper has better performance， and also provides an improvement for GBDT to predict other types of data.

Key words: load time series, STL decomposition, gradient boosting decision tree, multi-component prediction, load forecasting