Abstract:

Energy conservation and emission reduction in enterprises are the frontier issues and research hot-spots in the way of China´s development. Comprehensive analysis and evaluation on the current situation of power consumption in enterprises is the premise and foundation for energy-saving transformations or energy conservation designs. Having described the common forecasting methods for electricity consumption, the shortcomings of constructing a prediction model taking Classification and Regression Tree （CART）algorithm as the weak learner are analyzed. To deal with the deficiency of the traditional AdaBoost algorithm focusing on the minimum prediction error rate only, an improved AdaBoost algorithm based on multi-parameter cost-sensitive coefficient learning is studied and proposed based on the essence of the algorithm. A regression prediction model constructed based on the improved AdaBoost algorithm can make short-term power consumption prediction according to real data, which verifies the improvement of the model´s performance.

Key words: electricity consumption forecast, cost-sensitive coefficient, data-driven, Classification and Regression Tree algorithm, AdaBoost algorithm