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.