Uncertainty from Model Calibration: Applying a New Method to Transport Energy Demand Modelling

Abstract

Uncertainties in energy demand modelling originate from both limited understanding of the real-world system and a lack of data for model development, calibration and validation. These uncertainties allow for the development of different models, but also leave room for different calibrations of a single model. Here, an automated model calibration procedure was developed and tested for transport sector energy use modelling in the TIMER 2.0 global energy model. This model describes energy use on the basis of activity levels, structural change and autonomous and priceinduced energy efficiency improvements. We found that the model could reasonably reproduce historic data under different sets of parameter values, leading to different projections of future energy demand levels. Projected energy use for 2030 shows a range of 44-95% around the best-fit projection. Two different model interpretations of the past can generally be distinguished: (1) high useful energy intensity and major energy efficiency improvements or (2) low useful energy intensity and little efficiency improvement. Generally, the first lead to higher future energy demand levels than the second, but model and insights do not provide decisive arguments to attribute a higher likelihood to one of the alternatives. Keywords

Evaluating sustainability transitions pathways: Bridging analytical approaches to address governance challenges

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Model projections for household energy use in India

“…With three calibration-parameters, four energy options, five urban and rural population quintiles and data for two years, this calibration process has many degrees of freedom. We applied an automated model calibration procedure, that repeatedly starts at different random locations in the parameter space and minimises the error between model results and observations (van Ruijven et al, 2009;van Ruijven et al, 2010). The best calibrated sets of parameter values have Root Mean Square Error values of 9% for urban and 2% for rural areas across all fuels and quintiles.…”

Don׳t just follow the sun – A global assessment of economic performance for residential building photovoltaics

“…Electricity prices were sourced for the base year 2012 [63][64][65][66][67] and grown over time using growth rates from the TIMER model [68], which are also used in the recent OECD Environmental Outlook [69]. For Brasilia and Doha, no wholesale prices are available.…”

“…2012 electricity price base values were sourced from various sources [16,63,64,[71][72][73][74][75][76][77][78][79][80][81] and also grown over time using growth rates from the TIMER model [68]. Since industry prices, unlike wholesale prices, are available for most locations, we have modeled wholesale prices as 40% of industry price.…”

“…Details are given in Table A1. Price development until 2036 was modeled by applying growth rates from the TIMER model [68], which are also used in the recent OECD Environmental Outlook [69].…”