What Do Hourly Performance Data on a Building Tell Us

Hourly performance data on a building contain valuable information on the dynamics of the building and of the HVAC systems. Quantities such as the building loss coefficient, solar gains, and the net effect of thermal masses and their couplings are all contained in the data. In order to extract this information, a suitable analysis method is essential. A qualitative approach is to look at plots ofvariables such as inside temperature, auxiliary energy, etc. over selected periods such as a sequence of clear days or a sequence of cloudy days. Since several other quantities such as outdoor temperature, internal gains, and thermostat set points govern the building performance, the qualitative approach does not unambiguously establish cause and effect relationships; therefore it is of limiteduse. A quantitative approach that only uses the total auxiliary energy, average temperatures, average solar radiation, etc., over a month or over the season dilutes the dynamical information through the time-averaging process. If, on the other hand, a detailed simulation such as DOE2.1 is to be used, the nonavailability of the detailed inputs makes it difficult to extract the dynamicalinformation. It is thus clear that time-averaging as well as the requirement of component/subcomponent level inputs should be avoided. In other words, an hourly simulation that accepts whole building (or zone) level inputs is needed. In this article an hourly simulation called BEVA (Building Element Vector Analysis) is outlined that accepts whole building (or zone) level inputs. The number ofthese inputs is small, and they can be obtained from suitable short-term hourly performance data. An illustrative application of the method for a passive residential building is given. In order that BEVA be a design tool (and thereby also provide a natural framework to account for differences in design and actual performance), it is pointed out that these parameters can be calculated from abuilding description, i.e., from component/subcomponent level inputs. Potential additional applications for BEVA include determining long-term performance from short-term performance data, as well as HVAC system diagnosis.