2016 ASHRAE Winter Conference

Automated detection of schedule- and operation-related energy savings opportunities in commercial buildings can help building owners lower operating expenses while also reducing the adverse societal impacts such as global greenhouse gas emissions. We propose automated methods of identifying certain energy efficiency opportunities in commercial buildings using only whole-building electricity consumption and local climate data. Our two-step approach uses piecewise linear regression and density-based clustering to detect both schedule- and operation-related electricity consumption faults. This paper discusses results obtained from applying this approach to two office buildings and two residential buildings meant to demonstrate our model’s effectiveness in identifying such energy efficiency opportunities. Ways by which the analysis results can be conveniently and succinctly presented to building managers and operators are also suggested.

On the establishment of a reliable baselines for energy savings estimation, one or more variables are usually used to determine a model by regression analysis. These regression models generally use one or more independent variables, such as outside air temperature (OAT), degree days, or combination of these with occupancy or humidity. Based in a calibrated multi-use building energy simulation in a hot and humid climates, in this paper the study of the influence of outside air intake fraction on the selection of the best parameter to develop change-point regression modeling for cooling and heating energy use was evaluated. A comparison among regressions based on three variables, two regularly used in measuring and verification (M&V) process – OAT and outside air enthalpy (OAE), plus the addition of an operational enthalpy was carried out. The study included variations of the outside air intake fraction in the range of 10% -100% and the development of the corresponding patterns of regression models for each of the parameters.

Energy modelling and optimization studies can facilitate the design of cost-effective, low-energy buildings. However, this process inevitably involves early assumptions of unknowns such as predicting occupant behavior, future climate and econometric assumptions. As presently practiced, energy modelers typically do not quantify the implications of these unknown into performance outcomes. This paper describes an energy modelling approach to quantify economic risk and better inform decision-makers of the economic feasibility of a project. The proposed methodology suggests how economic uncertainty can be quantified within an optimization framework. This approach improves modelling 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 located in London, ON.

There is a trend towards high-rise residential buildings with large glazed areas – often floor-to-ceiling. In most climates, these buildings are reliant on mechanical systems to maintain comfort as a result of the poor insulating properties and high solar transmittance of the glazing. In the summer they are prone to overheating from high solar gains; in the winter, they are prone to thermal discomfort due to low surface temperatures and high heat loss through poorly-insulated glazing and other façade components. Thus, such buildings are vulnerable to power failures, mechanical system failures, and extended demand response strategies. Furthermore, such buildings can be uncomfortable and high in energy consumption during normal operation. This paper describes a methodology to evaluate building resilience using simulation methods. Several resilience metrics were developed or obtained from the literature including: thermal autonomy and passive survivability. A Toronto, Canada-based case study was performed to assess the effect of various passive design strategies to improve resilience. Results showed that thermal autonomy was very poor without occupant interaction. However, this did not translate to poor passive survivability; relatively comfortable conditions were maintained for at least two to three days after power failures.

We introduce and provide results from a rigorous, scientific testing methodology that allows pure building model calibration systems to be compared fairly to traditional output error (e.g. how well does simulation output match utility bills?) as well as input-side error (e.g. how well, variable-by-variable, did the calibration capture the true building's description?). This system is then used to generate data for a correlation study of output and input error measures that validates CV(RMSE) and NMBE metrics put forth by ASHRAE Guideline 14 and suggests possible alternatives.