In this paper multivariate statistical techniques are used to analyze the data output of partial equilibrium energy models developed in the framework of the NEEDS Project, with the aim of emphasising their informational content and reducing redundancies. In particular, Cluster Analysis and Principal Component Analysis are applied to characterise final energy consumption and CO2 emission by country for two different scenarios (Business as Usual – BAU and CO2_450ppmv), and with reference to years 2000, 2015 and 2050. The overall objective is to set up a general applica-ble procedure for characterizing data correlation structure and identifying suited indicators, in order to devise ad-vanced tools for supporting decision making processes as well as for assessing the sustainability of energy-environ-mental strategies.
Multivariate techniques for the analysis of partial equilibrium energy models results
RAGOSTA, Maria
2008-01-01
Abstract
In this paper multivariate statistical techniques are used to analyze the data output of partial equilibrium energy models developed in the framework of the NEEDS Project, with the aim of emphasising their informational content and reducing redundancies. In particular, Cluster Analysis and Principal Component Analysis are applied to characterise final energy consumption and CO2 emission by country for two different scenarios (Business as Usual – BAU and CO2_450ppmv), and with reference to years 2000, 2015 and 2050. The overall objective is to set up a general applica-ble procedure for characterizing data correlation structure and identifying suited indicators, in order to devise ad-vanced tools for supporting decision making processes as well as for assessing the sustainability of energy-environ-mental strategies.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
