Modelling uncertainty in district energy simulations by stochastic residential occupant behaviour. Issue 4 (3rd July 2016)

• Record Type: Journal Article
• Authors: Baetens, Ruben; Saelens, Dirk
• Is Part Of: Journal of building performance simulation. Volume 9:Issue 4(2016)
• Page Start: 431
• Page End: 447
• Publication Date: 2016-07-03
• Subjects: district simulation -- stochastic modelling -- occupant behaviour -- uncertainty; 690.0113
• DOI: 10.1080/19401493.2015.1070203
• Languages: English
• ISSNs: 1940-1493

Abstract

Abstract : Occupant behaviour has long been of main interest in the domain of building energy-savings and indoor air quality and its importance is recognized by its wide coverage in the literature. In the recent developments of detailed transient building energy simulations, including the occupant behaviour as a boundary condition for the thermal comfort, system efficiency calculations have been a major research topic, given its significant impact. A simultaneous growing interest in district energy simulations raises similar questions at the aggregate level, where upscaling from the building to an aggregate neighbourhood level at the spatial scale of a low-voltage feeder results in a natural regression to the mean lowering uncertainty, compared to the level of the household. The presented work starts with the description ofStROBe, a stochastic residential occupant behaviour for district energy simulations integrating the modelling of receptacle loads, internal heat gains, thermostat settings and hot water tappings based on occupancy and activity prerequisites. Given this model, the uncertainty for district energy simulations is addressed. The epistemic uncertainties are elaborated first by comparing the model results with the reference values and then denoting local disaggregation of demographic statistics as a possible main hiatus of general modelling methods for building energy occupant behaviour used at the neighbourhood level. Finally, the aleatory uncertainty caused by the StROBe in integrated district energy simulations is quantified. Here, the expected value of the objective functions has, to a large extent, the same minimizers as the measures of the proposed robustness. As such, optimizing an objective value for its expected value generally seems to result in a optimum near the optimum of robustness. However, 95% of the observed objectives lay between 0.81 and 1.6 times the expected value for a feeder larger than 10 houses, and between 0.88 and 1.3 times the expected value for more than 20 houses, denoting an overall 'rather small' uncertainty on the possible objective functions caused by the user behaviour. Furthermore, we show that the design of the building energy system has an impact on the robustness of the objective criteria and it could thus be minimized as part of an optimisation exercise.