In building performance simulation, understanding the potential for parameter variations to cause a disproportionately large change in a performance metric is an important aspect of the modelling and design process. This is especially true if the proposed building is expected to meet a performance target such as net-zero energy consumption. In the context of this paper, variations refer to design modifications which lead to large changes in a performance metric. This paper proposes a methodology to identify influential variations around a performance criterion. This methodology aids in the understanding of possible discrepancies between predicted and realized building performance. A net-zero energy house case-study demonstrates the methodology. A variability analysis of the case-study indicated that combinations of variations caused energy consumption to be larger than on-site generation in 20% of variational scenarios. A back-tracking search identified that 8 of 26 variables were responsible for significant changes in net-energy consumption. In particular, energy-related occupant behaviour, solar orientation, and variables related to the sizing of a roof-based photovoltaic system can significantly influence net-energy consumption. The case-study helped quantify two optimal approaches for passive solar design -one relying on high insulation levels and lower window areas, and the other relying on good insulation levels and large window areas.

This paper applies an energy optimization methodology to identify improvements to a monitored near net-zero energy house located near Montreal, Canada. The method leverages design variable interdependencies to expedite the optimization process via a hybrid deterministic-evolutionary algorithm. This paper presents several recommendations and lessons learned, which can be applied to the design optimization of net-zero energy homes.

No abstract

Automated mathematical building performance optimization (BPO) paired with building performance simulation (BPS) is a promising solution for evaluating many different design options and obtaining the optimal or near-optimal solution for a given objective or combination of objectives (e.g., lowest life-cycle cost, lowest capital cost, highest thermal comfort) while complying with constraints (e.g., net zero-energy) (

No abstract