IBPSA2013

This paper presents a techno-economic analysis of deep energy retrofit strategies aimed at improving a typical existing home to a Net Zero Ready (NZR) level. Three distinct pathways are selected to examine the impact of modifying the mechanical system and building envelope. Each pathway is analyzed in TRNSYS for both the Montreal and Vancouver regions using a validated housing model. A techno-economic analysis methodology then combines the calculated annual energy costs with the associated material and labour costs for each option.

The climate indicators that are currently used in the building energy area, summations of degree-days, are not suitable for net-zero and low energy analysis, because they fail to characterize the building-climate interaction. This paper presents a new set of climate indicators that focuses on overall climate and building performance, as well as specific climate statistics that have a relevant impact in NZEB and passive buildings. The proposed framework is based on three interrelated energy performance indicators.

The application of building simulation and modelling is becoming more widespread, particularly in the anal-ysis of residential buildings. The energy consump-tion and control of systems in residential buildings are tightly linked to the behaviour of people, arguably more so than in commercial buildings which have tra-ditionally been the preserve of building simulation analysis. The input profiles used in simulation pay little attention to the link between numerical charac-terisations of observed ‘behaviour’ and the way people actually live in the home.

The urban microclimate (UMC) can strongly affect the building energy demand. In this paper, the impact of the UMC on the space heating and cooling energy demand of buildings is analysed for typical office buildings in street canyon configurations, using detailed building energy simulations (BES). Convective aspects of the UMC are modelled using computational fluid dynamics (CFD) and data are transferred to BES, either by convective heat transfer coefficients or by directly coupling CFD and BES. Measured urban heat island intensities are additionally considered.

Monitoring energy and temperatures in dwellings is becoming commonplace due to the reduction in sens-ing costs. Measurements can be used for informing the occupants on their energy as well as developing better inputs for building performance simulation and verifying analysis. In a home monitoring environment making sense of this data can be difficult as the number of measurements increases; one of the key challenges for the homeowner and for organisations that collect and analysis energy data is understanding what can and cannot be ‘seen’ in the data.