A prediction method for provincial power substitution potential based on Lasso-XGBoost-Stacking

Abstract

In order to better grasp the quantitative potential of provincial power substitution， a prediction model based on Lasso-XGBoost-Stacking is proposed. Five influencing factors， including economic development， environmental protection， energy price， policy support and technological progress， are quantified by cross-features to reduce the multicollinearity among the influencing factors. Besides， the weights of the quantified influencing factors are evaluated by using Lasso regression model. The predicted MAPE （mean absolute percentage error） of Zhejiang province is up to 12.22%， which can meet the requirements of quantitative potential analysis of power substitution in the province. The analysis of the multi-scenario power substitution scenarios in Zhejiang reveals that economic development has the greatest impact on power substitution potential， and the transportation industry has the greatest power substitution potential relative to industry， agriculture and other sectors.

Keywords

• power substitution
• lasso regression
• xgboost
• stacking
• quantified influencing factors