Adaptive Combination Forecasting Model Based on Area Correlation Degree with Application to China's Energy Consumption

To accurately forecast energy consumption plays a vital part in rational energy planning formulation for a country.This study applies individualmodels (BP, GM (1, 1), triple exponential smoothingmodel, and polynomial trend extrapolationmodel) and combination forecastingmodels to predict China’s energy consumption. Since area correlation degree (ACD) can comprehensively evaluate both the correlation andfitting error of forecastingmodel, it ismore effective to evaluate the performance of forecastingmodel. Firstly, the forecastingmodel’s performances rank in linewithACD.ThenACD is firstly proposed to choose individualmodels for combination and determine combination weight in this paper. Forecast results show that combination models usually have more accurate forecasting performance than individualmodels.The newmethod based onACD shows its superiority in determining combination weights, compared with some other combination weight assignment methods such as: entropy weight method, reciprocal of mean absolute percentage error weight method, and optimal method of absolute percentage error minimization. By using combination forecasting model based on ACD, China’s energy consumption will be up to 5.7988 billion tons of standard coal in 2018.

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