An application of multivariate statistical techniques to partial equilibrium models outputs: The analysis of the NEEDS-TIMES Pan European model results

Senatro Di Leo, Carmelina Cosmi and Maria Ragosta

Renewable and Sustainable Energy Reviews, 2015, vol. 49, issue C, 108-120

Abstract: Sustainable development requires analytical tools to assess through a comprehensive approach the effectiveness of energy-environmental policies on medium-long term and their impact on the different macroeconomic sectors. Among the available tools, partial equilibrium models are particularly suited to represent and analyze complex energy systems with a high technology detail as well as to individuate the optimal energy-technology roadmaps that allows to fulfil multiple objectives (e.g. energy supply security, climate change mitigation and air quality improvement). In fact these models enable the users to perform a scenario analysis in order to explore the behaviour of energy system to boundary conditions variations. They are typically characterized by a huge amount of data which informative content is often not fully exploited. In this framework, multivariate statistical techniques (Cluster Analysis—CA and Principal Component Analysis—PCA) can represent a key tool to characterize data correlation structure and to point out the variables with the highest information content. The paper shows the methodological procedure utilized to characterize the NEEDS-TIMES Pan European model results, describing its usefulness to individuate homogeneous areas for the application of suited targets and to characterize from a statistical point of view energy systems behaviour in different scenario hypothesis.

Keywords: Energy models; Multivariate statistical analysis; Synthetic index (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1016/j.rser.2015.04.099

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