Modeling a hybrid methodology for evaluating and forecasting regional energy efficiency in China

• Published in: Applied energy 2017-01, Vol.185, p.1769-1777
• Main Authors: Li, Ming-Jia, He, Ya-Ling, Tao, Wen-Quan
• Format: Article
• Language: English
• Subjects: Cluster areas; Energy efficiency indicator; GARCH model; Radial basis function neural; SFA model
• ISSN: 0306-2619; 1872-9118
• DOI: 10.1016/j.apenergy.2015.11.082

[Display omitted] •A new hybrid methodology consists of SFA-GARCH model and RBFN model is structured.•Regional energy efficiency in China is measured during 2003–2014.•Short-term forecast is examined without manual intervention from 2016 to 2020.•The hybrid methodology avoids the superposition of errors of the individual forecasts.•The 30 regions in China are clustered into high, moderate and low efficiency areas. This study proposes a new hybrid methodology for short-term prediction of energy efficiency. This new method consists of the stochastic frontier analysis-generalised autoregressive conditional heteroskedasticity (SFA-GARCH) model and the radial basis function neural (RBFN) model. The study finds that 30 regions (provinces and municipalities) in China have cluster-hetergeneity, and the different levels of industry structure, technology content and energy resources in the different regions lead to dissimilar energy saving quotas. In addition, through fair comparison between the traditional GARCH model and the new hybrid model, it is proved that the new hybrid model shows good performance and the results are reasonable. The energy efficiency indicators predicted by the hybrid model appear to be more reliable than the summation of the individual forecasts because it avoids the superposition of errors.