مقاله انگلیسی رایگان در مورد یک مدل جهانی برای استفاده از انرژی در مناطق مسکونی – الزویر 2010

Abstract

Uncertainties in energy demand modelling allow for the development of different models, but also leave room for different calibrations of a single model. We apply an automated model calibration procedure to analyse calibration uncertainty of residential sector energy use modelling in the TIMER 2.0 global energy model. This model simulates energy use on the basis of changes in useful energy intensity, technology development (AEEI) and price responses (PIEEI). We find that different implementations of these factors yield behavioural model results. Model calibration uncertainty is identified as influential source for variation in future projections: amounting 30% to 100% around the best estimate. Energy modellers should systematically account for this and communicate calibration uncertainty ranges.

Introduction

Developments of the energy system play a key role in economic development and environmental problems at different scales. This includes issues like access to modern energy, securing energy supply and environmental problems, such as air pollution and climate change. It is therefore important to explore different potential development paths of the energy system. However, at least two factors complicate projections of future energy use: (1) the energy system is determined by complex interactions of a wide range of drivers and (2) there is a lack of empirical data. This lack of information complicates the development and calibration of models, especially for developing regions – and allows for multiple interpretations of the same phenomena. Despite these difficulties, a wide range of models has been developed to explore trends in the energy system at global [1–5], regional [6–8] and national scales [6,7,9,10]. These models are partly developed from different scientific paradigms and modelling traditions. Such paradigms may lead to different interpretations of the past and different expectations for the future. The most clear-cut example is the difference between models that stem from a macro-economic tradition (top-down) [11,12] and those from a engineering– economic tradition (bottom-up) [6,7,13] that lead to different interpretations of the present situation with respect to energy efficiency (optimal vis-a` -vis major opportunities for improvement) [14,15]. Even within one model, however, several options may exist on how to interpret the past and current situation. This may lead to different model calibrations, that cause uncertainty in future projections.