Referenced by HPBMS.com
There is emerging evidence that the AEC industry needs fundamentally new methods to respond to requirements for efficiency, effectiveness and performance relative to facility sustainability and energy usage. For example, when comparing predicted and measured energy usage values, measured use systematically and dramatically exceed objectives / predicted values.
The Stanford University Jerry Yang and Akiko Yamazaki Environment and Energy Building (Y2E2) completed its first full year of operation in 2008.
The 166K square foot building was designed to accommodate a multidisciplinary set of researchers and students from several schools departments and “inspire faculty, staff, students and visitors to take the next steps toward a sustainable future.”
Following analysis of energy simulation predictions based on a building information model (BIM), building designers added energy saving features including natural ventilation, heat recovery, central atria for light and circulation, and “night flushing” or opening rooftop windows in the atria to allow hot building air to escape to the outside on cool evenings to be replaced with outside air. In addition, the building was built with 2,370 HVAC system measurement points each of which is sampled by a computer‐based data collection system each minute or 1,440 times per day, which represents about 3.5M samples/day for the building.
We led the Stanford CEE243 graduate class in the Spring of 2009 that analyzed (some of) the measured building energy system data, made predictions using energy analysis tools, compared measured, predicted and expected data value, attempted to interpret measured values as conforming or not to design intent, and made some recommendations to the owner.
Findings of the class study included that students with no prior background could successfully access and interpret measured energy performance data from the data acquisition computer; overall building energy use met code objectives but dramatically exceeded initial design objectives; some HVAC components and systems worked well and others did not work as planned, and a gifted set of eleven students together worked about a thousand hours to interpret only about ten percent of the available data, which strongly indicates that the current process to access and interpret data is not sufficiently routine and automated to allow effective continuous energy system commissioning on a significant commercial scale.