几何转换Boosting回归算法及其在高耗能企业负荷预测中的应用

针对高耗能企业的电力负荷预测问题, 提出了一种基于几何转换关系的Boosting集成预测方法. 该方法将分类问题中广泛采用的AdaBoost算法引入回归问题, 通过几何关系转换将负荷预测这一回归问题转化为一个两类分类问题, 在此分类问题上应用置信度AdaBoost算法得到最优集成分类面. 证明了此分类面等价于原回归问题上的一个回归函数, 同时证明了该方法与原始AdaBoost算法有着类似的收敛性. 实际算例表明, 该方法通过多预测模型的集成有效提高了负荷预测精度, 克服了传统单一预测模型在高耗能企业的电力负荷预测问题上泛化性能不佳的缺陷.

Boosting regression method based on geometric conversion and its application to load forecasting in energy-intensive enterprise

Focus on the electricity load forecasting in energy-intensive enterprise, a novel Boosting forecasting method based on geometric conversion is proposed. This method extends the AdaBoost algorithm to the regression problem. In this method, a regression problem such as load forecasting is converted to a binary classification problem through geometric operation. Then confidence-rated AdaBoost is implemented on this classification problem and an ensemble separating plane is obtained. This paper proves that such a plane is identical to a regression function for original load forecasting problem, and also proves that the proposed method has theoretical convergence. Simulation results state that compared to some popular single forecasting models, the proposed method improves the accuracy of the load forecasting in energy-intensive enterprise.