Abstract
Domestic energy modelling is complex, in terms of user input and the approach used to define the model; therefore, there is an increase in the sources of uncertainties. Previous efforts to perform sensitivity and uncertainty analyses have focused on national energy models, while in this research, the objective is to extend traditional sensitivity analysis and use a local framework combining principal component regression and Monte Carlo Simulation. Therefore, in our method the total amount of the energy output’s variance is decomposed, in relative terms, according to the contribution of the different predictor parameters. Our framework provides compelling evidence that local area characteristics are important in energy modelling and those national and regional indexes and values may not properly reflect the local conditions, resulting in programmes and interventions that will be sub-optimal. Furthermore, our uncertainty methodology uses a three dimensional integrative taxonomy and a concept map. The concept map identified concrete terminal causes of uncertainty within the taxonomic framework of sources, issues, sub-issues and a further abstraction of those quantities in terms of accuracy and precision. Understanding uncertainties in this way provides a possible framework for modellers, policy makers and data collectors to improve practice in key areas and to reduce uncertainty.
Highlights
Despite the importance of the domestic energy modelling in sub-city areas, the energy sector lacks a rigorous analytical framework to account for the uncertainties
Energies 2017, 10, 1986 sensitivity analysis in order to decompose the pathway as uncertainty flows through the dynamics and identified which internal or intermediate processes transmit the most uncertainty to the final output [1]; Grömping assigned shares of “relative importance to each of a set of regressors when applying linear regression” [2] and Nguyen et al employed several sensitivity analysis methods commonly used in building simulation to assess the significance of various input parameters in specific mathematical models and computer building energy models [3]
Summerfield et al argue that uncertainty appears in domestic energy modelling as follows: (i) input data, both in terms of the accuracy of the individual input entry and the range of values associated with a particular building component; (ii) in assumptions about the energy calculation engine, or inaccuracies in the values implicitly assumed in the calculation for the weather characteristics surrounding the building, among other assumptions; (iii) in differences between the measures ‘as modelled’ and the specification ‘as constructed’; (iv) in differences in the occupancy patterns; and; (v) in issues with post-occupancy surveying and monitoring of the building [4]
Summary
Despite the importance of the domestic energy modelling in sub-city areas, the energy sector lacks a rigorous analytical framework to account for the uncertainties. In addition to the taxonomic structure, due to variations in energy phenomena that need to be numerically calculated, this paper considers data distributions reflecting the uncertainty available to allow the use of numerical simulation methods for uncertainty quantification and propagation. The principle of Monte Carlo Simulation is the propagation of the input (predictors) probability distributions through the model This provides a general probabilistic basis for uncertainty evaluation in the energy estimation outcome. NCRF utilises this data set and adds to the energy modelling aspect through linking with the English House Survey (EHS) as input to the Cambridge Housing Model (CHM) This provides the means to produce building-level energy consumption estimates which in turn can be analysed both spatially and aspatially (e.g., by building type)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
