The College Station Residential Energy Compliance Code

The City of College Station, Texas adopted a new residential Energy Compliance Code in January, 1988. The code, which strengthens compliance requirements in several areas, has received broadly based support and acceptance from all major constituent groups. It is less than one-fourth the length of the code it replaced, and compliance is greatly simplified through use of a check-list compliance path supplemented by point system and energy analysis paths. Results of air leakage measurements used to justify the stronger infiltration requirements of the code are reported. The process used to develop consensus support and key features of the code are described

Lessons Learned from Continuous Commissioning® of a LEED Gold Building in Texas

The subject building is a relatively new building with 120,000 square feet located in Texas and was the first LEED® Gold building in the area. To earn the title of a green building, the designers of this high performance building included many conservation and energy related design features and construction practices. The energy related design features of the building include a roof mounted photovoltaic system, a green roof design, and connection to a district cooling system which utilizes thermal storage. Many of the operations and mechanical issues identified during the course of commissioning the subject building are items common to many commercial buildings, green or conventional. The potential cost savings from implementing the measures is 21% of the annual energy consumption with a simple payback of less than one year. The findings at the subject building suggest that: • High performance buildings have many of the same problems as conventional buildings since none of the issues and opportunities identified would generally be considered unique to high performance buildings • The potential for savings from commissioning the systems in high performance buildings is similar to that of conventional buildings and is as economically attractive

Impact of Nighttime Shut Down on the Prediction Accuracy of Monthly Regression Models for Energy Consumption in Commercial Buildings

Regression models of measured energy use in buildings are widely used as baseline models to determine retrofit savings from measured energy consumption. It is less expensive to determine savings from monthly utility bills when they are available than to install hourly metering equipment. However, little is known about the impact of nighttime shut off on the accuracy of savings determined from monthly data. This paper reports a preliminary investigation of this question by comparing the heating and cooling energy use predicted by regression models based on monthly data against the predictions of calibrated hourly simulation models when applied to a medium-sized university building in Texas with (i) DDCAV system operating 24 hours per day, (ii) DDCAV system with nighttime shut down, (iii) DDVAV system operating 24 hours per day, and (iv) DDVAV system with nighttime shut down. The results of the four cases studied indicate : 1) when the AHUs are operated 24 hours/day, the annual prediction error of the cooling regression models is less than 0.5% of the annual cooling energy consumption; however, 2) when the AHUs are operated with nighttime shut down, the annual prediction error of the cooling models becomes as high as 6% of annual energy consumption. It should be noted that the cases considered here include only single end-uses of energy and have not investigated energy-use data which includes multiple end-uses. Modified regression models are therefore recommended when AHUs are not operated 24 hours per day and the temperature pattern is significantly different between pre and post retrofit years

Lessons Learned from Continuous Commissioning of the Robert E. Johnson State Office Building, Austin, TX

The Robert E. Johnson State Office building is a 5-story, 303,389 square foot office building built in 2000 located in downtown Austin, TX. The original building design included a number of energy conservation measures that were incorporated into the final construction. During the investigation of the building, four energy conservation measures were identified, three of which deal with conventional HVAC systems. The fourth is related to the currently unutilized daylighting system which was one of the energy conservation measures of the original building design. Utilizing this system would lead to approximately 18.5% annual lighting energy savings or 5.6% annual whole building energy savings based on a DOE-2 simulation analysis. Three main lessons were learned from the experience with the Robert E. Johnson building: • The traditional design-construction-operation team must include the energy conservation analysis team • The entire building process should be reorganized to assure that complete information is provided and passed on from the energy conservation analysis team • High performance buildings should be continuously monitored and analyze

Bridging the Gap Between Commissioning Measures and Large Scale Retrofits in Existing Buildings

Most often commissioning of existing buildings seeks to reduce a building’s energy consumption by implementation of operational changes via the existing equipment. In contrast, large scale capital retrofits seek to make major changes to the systems installed in the building to reach the same goal. The purpose of the investigations presented here is to find energy-saving measures which economically fall between the retro-commissioning measures which typically have very short paybacks and the large scale capital retrofits which typically have significantly longer paybacks. Based on a simulation analysis of three previously retro-commissioned university buildings, it was determined that all three are currently consuming more energy than would be expected under ideal operating conditions. The simulation estimated annual savings potential for the three buildings range from 28-44% of whole building energy consumption. A research level assessment of each has been conducted to identify the reasons why the subject buildings are not operating as efficiently as possible and energy saving measures are presented to bring the buildings as close to ideal operation as possible. This work seeks to determine if an on-site assessment can identify commissioning measures that realize a substantial portion of the indicated savings potential or whether it appears that there are reasons that would preclude commissioning measures from achieving significant savings. If it is not practical to implement commissioning measures due to antiquated controls, missing sensors, or other reasons, these investigations identify rapid payback retrofit measures that achieve as much of the projected savings as possible. The analysis indicates that 30-100% of the estimated savings potential can be realized in the three subject buildings with estimated paybacks of less than 3 years

Using Simulation Models for Building Commissioning

The International Energy Agency ECBCS Annex 40 “Commissioning of Buildings and HVAC Systems for Improved Energy Performance” task investigating Use of Whole Building Simulation in Commissioning has identified the following applications of whole simulation in the commissioning process: 1) during the design process; 2) in post-construction commissioning of new buildings; 3) design simulation for ongoing commissioning; 4) calibrated simulation for retro commissioning; 5) calibrated simulation for on-going commissioning; and 6) simulation to evaluate new control code. These applications are discussed and examples of each of these applications are provided. The only one of these which has been applied in routine commissioning projects is the use of calibrated simulation for retro commissioning. The other examples have been applied in a research setting, and costs must be lowered for routine application, but there appears to be potential for significant application of simulation in the commissioning process

Methodologies for Determining Persistence of Commissioning Benefits

Studies on the persistence of commissioning benefits to date have used a variety of methods to evaluate this persistence. This paper proposes a consistent framework for describing and evaluating the persistence of commissioning benefits. It begins by splitting commissioning benefits into two broad categories: 1) benefits that inherently persist; and 2) benefits that may not persist. The study of persistence then considers only the benefits that may not persist. These benefits are critical, since the top five reasons cited for performing commissioning in both new buildings and existing buildings are benefits that may not persist. These benefits are then further divided into benefits that may be quantified and benefits that are generally difficult to quantify. This paper proposes that benefits that may be quantified should generally be evaluated for persistence using approaches that are already widely accepted and used for other purposes, with adaptations where needed. Specifically, it proposes that energy and water savings be evaluated using methods consistent with the International Performance Measurement and Verification Protocol (adapted with additional weather normalization), that comfort and indoor air quality improvements be evaluated using relevant standards, specifically ASHRAE Standard 55 and ASHRAE Standard 62, but goes further and proposes a methodology for economic quantification of these benefits as well. Finally, it is proposed that the persistence of measures whose benefit is difficult to quantify be evaluated simply by determining whether the measure is still in place or performing

Accounting for the Occupancy Variable in Inverse Building Energy Baselining Models

The occupancy factor is often underestimated in inverse modeling of building energy use, or accounted for by grouping the daily data in occupied and unoccupied groups which are modeled separately. For instance, in institutional buildings it is common to identify "weekdays/weekends", "semester breaks", and "holidays" daytypes. In order to develop one model that accounts for all periods, i.e., occupied and unoccupied, at an hourly time scale, a dummy variable (regressor) can be used. The dummy variable is often used in a simplified way; for instance, having a value of 0 between 8:00 AM and 5:00 PM, and 1 between 5:00 PM and 8:00 AM, for an office building. In this paper, the effect of using different alternatives in accounting for the occupancy variable in inverse modeling of building energy use is investigated, and the resulting uncertainty in the predictions, using the SMLP inverse method are presented