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

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Bibliographic Details
Title:	Adaptive Combination Forecasting Model Based on Area Correlation Degree with Application to China's Energy Consumption.
Authors:	Zhou Cheng¹, Chen XiYang² chenxiyang@mail.hust.edu.cn
Source:	Journal of Applied Mathematics. 2014, p1-12. 12p.
Subjects:	Adaptive computing systems, Energy consumption, Weather forecasting, Statistical smoothing, Statistical correlation
Geographic Terms:	China
Abstract:	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 and fitting error of forecastingmodel, it ismore effective to evaluate the performance of forecastingmodel. Firstly, the forecastingmodel's performances rank in line with ACD. Then ACD 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 individual models. The new method based onACDshows 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. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	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 and fitting error of forecastingmodel, it ismore effective to evaluate the performance of forecastingmodel. Firstly, the forecastingmodel's performances rank in line with ACD. Then ACD 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 individual models. The new method based onACDshows 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. [ABSTRACT FROM AUTHOR]
ISSN:	1110757X
DOI:	10.1155/2014/845807