Energy demand/Data uncertainties limitations: Difference between revisions

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|Reference=Schaeffer et al.,????; Daioglou, 2010; van Ruijven et al., 2010a; van Ruijven et al., 2010b
|Reference=Schaeffer et al.,????; Daioglou, 2010; van Ruijven et al., 2010a; van Ruijven et al., 2010b
|Description=<h2>Data</h2>
|Description=<h2>Data</h2>
The energy demand models are calibrated against historic [[hasAcronym::IEA]] data for the period 1971-2007. This calibration aims to simulate the historic trends in energy demand and fuel choice, but does not aim to reproduce historic energy use exactly. The (bottom-up) models use data from other sources as well. For instance, historic demand and trade of steel and cement are taken from XXXX and YYYY, historical income data from the World Bank and transport data from Schaeffer et al. (). The residential model uses data from many different national statistical agencies and household survey, an overview of which can be found in (Daioglou, 2010).


==Uncertainty analysis==
==Uncertainty analysis==
Important uncertainties in energy demand modeling relate to the interpretation of historical trends (the role of structural change, autonomous energy efficiency improvement and price-induced efficiency improvement) and the interpretation of these trends for the future. In that context, it should be noted that system-dynamic models, like TIMER, generally assume that demand for energy services will saturate at some point (and find evidence in trends in certain sectors, e.g residential energy use). Economic models, in contrast, assume that income and energy use remain coupled (and also find evidence for that in econometric relationships between historic data). In deciding between these different interpretation it is even uncertain how much historical trends should be taken as guiding future trends. Similarly, uncertain is the description of efficiency improvement. While empirical research suggests it is possible to significantly improve efficiency, historically, the investments in efficiency have been limited. As a result, it is highly uncertain whether end-users will invest in efficiency, and what the associated costs are.


On the part of the uncertainty that is related to model calibration, we developed some methods to identify the implications of different model calibration and applied this method both to the transport model and the residential model (van Ruijven et al., 2010a; van Ruijven et al., 2010b). Starting point is the notion that there insufficient data available to fully understand historical trends and calibrate of global energy models. In our work, we have show that for the TIMER model there can be room for different sets of parameter values that simulate historic energy use equally well, but based on different historic interpretations and resulting in different future projections. By relating energy demand to different end-use functions it is possible to somewhat reduce uncertainty compared to description that only take monetary indicators into account.
 
 
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