Energy models are commonly used to examine the multitude of pathways to improve building performance. As presently practiced, a deterministic approach is used to evaluate incremental design improvements to achieve performance targets. However, significant insight can be gained by examining the implications of modeling assumptions using a probabilistic approach. Analyzing the effect of small perturbations on the inputs of energy and economic models can improve decision making and modeler confidence in building simulation results. This paper describes a reproducible methodology which AIDS modelers in identifying energy and economic uncertainties caused by variabilities in solar exposure. Using an optimization framework, uncertainty is quantified across the entire simulation solution space. This approach improves modeling outcomes by factoring in the effect of variability in assumptions and improves confidence in simulation results. The methodology is demonstrated using a net zero energy commercial office building case study.
Net-zero energy is an influential idea in guiding the building stock towards renewable
energy resources. Increasingly, this target is scaled to entire communities
which may include dozens of buildings in each new development phase.
Although building energy modelling processes and codes have been well developed
to guide decision making, there is a lack of methodologies for community
integrated energy masterplanning. The problem is further complicated by the
availability of district systems which better harvest and store on-site renewable
energy. In response to these challenges, this paper contributes an energy modelling
methodology which helps energy masterplanners determine trade-offs between
building energy saving measures and district system design. Furthermore,
this paper shows that it is possible to mitigate electrical and thermal peaks of a
net-zero energy community using minimal district equipment. The methodology
is demonstrated using a cold-climate case-study with both significant heating/
cooling loads and solar energy resources.
This paper presents a multi-objective redesign case study of an archetype solar house based on a near net zero energy (NZE) demonstration home located in Eastman, Quebec. Using optimization techniques, pathways are identified from the original design to both cost and energy optimal designs. An evolutionary algorithm is used to optimize trade-offs between passive solar gains and active solar generation, using two objective functions: net-energy consumption and life-cycle cost over a thirty-year life cycle. In addition, this paper explores different pathways to net zero energy based on economic incentives, such as feed-in tariffs for on-site electricity production from renewables. The main objective is to identify pathways to net zero energy that will facilitate the future systematic design of similar homes based on the concept of the archetype that combines passive solar design; energy-efficiency measures, including a geothermal heat pump; and a building-integrated photovoltaic system. Results from this paper can be utilized as follows: (1) systematic design improvements and applications of lessons learned from a proven NZE home design concept, (2) use of a methodology to understand pathways to cost and energy optimal building designs, and (3) to aid in policy development on economic incentives that can positively influence optimized home design.
Net zero energy (NZE) communities are becoming pivotal to the energy vision of developers. Communities that produce as much energy as they consume provide many benefits, such as reducing life-cycle costs and better resilience to grid outages. If deployed using smart-grid technology, NZE communities can act as a grid node and aid in balancing electrical demand. However, identifying cost-effective pathways to NZE requires detailed energy and economic models. Information required to build such models is not typically available at the early master-planning stages, where the largest energy and economic saving opportunities exist. Methodologies that expedite and streamline energy and economic modeling could facilitate early decision making. This paper describes a reproducible methodology that aids modelers in identifying energy and economic savings opportunities in the early community design stages. As additional information becomes available, models can quickly be recreated and evaluated. The proposed methodology is applied to the first-phase design of a NZE community under development in Southwestern Ontario.