2016 ASHRAE Annual Conference

The purpose of Research Project RP1627 is to determine the effectiveness of 30% Advanced Energy Design Guidelines for K-12 schools and small office buildings, determine the factors common to well and poorly performing buildings, and to provide recommendations for how future AEDGs could be made more effective. Group14 collected utility data and developed weather-normalized Energy Utilization Indices (EUIs; site energy use per unit area per year) for a sample of small office and K-12 school buildings designed in accordance with the first (30%) ASHRAE AEDGs. The results were compared to the modeled ASHRAE Standard 90.1-1999 Baseline and 30% Savings EUIs from the Technical Support Documents used to develop the two AEDGs. The sample included 30 schools and 23 office buildings; most designed to AEDG requirements. A total of 14 of the 53 building sample were designed to meet the ASHRAE Standard 90.1-1999 code; these code buildings provide a means for comparison to the AEDG buildings. Most buildings meeting 30%AEDG requirements achieved energy savings exceeding 50% of the ASHRAE 90.1 Baselines. However, non-AEDG schools, constructed to code, performed nearly as well. Most school districts are committed to energy-efficient buildings. The non-AEDG small office average EUI was near the Baseline EUI, but AEDG small offices EUIs were about 50% of Baseline EUIs, indicating significantly better performance for AEDG small office buildings. On-site energy and indoor environmental quality (IEQ) audits were performed on 5 schools and 5 small office buildings with different designs and in different climate zones to verify AEDG required strategies were included in the designs and that strategies are operational and effective. In general, most, but not all, AEDG strategies were in place and operational. The research project is still underway and is scheduled for completion in January 2016. The next steps are: to determine the factors common to relatively well-performing buildings, as well as the factors common to relatively poorly-performing buildings, based on building surveys.  The next step is to provide recommendations for how future AEDGs for small office and K-12 school buildings could be made more effective in achieving better energy performance than required by ASHRAE Standard 90.1 while providing acceptable indoor environmental quality. This project supports goals of ASHRAE’s Research Strategic Plan 2010 – 2015 and will help ASHRAE maintain its leadership position in the effort to help engineers, designers, and contractors build progressively more energy-efficient buildings that deliver acceptable indoor environmental quality at a reasonable cost.

The Energy Policy Act of 2005 (EPACT 2005) required energy use in federal buildings to be metered. Since then, the US Army has installed electricity and natural gas meters on new and many existing facilities. To manage vast amounts of metered data from installations across the country, a centralized Metering Data Management System (MDMS) was put into place. MDMS continuously collects metered data, and makes it available at a central location for easy access. However, it quickly became clear that for the building energy data to be useful, it had to be put into context. CBECS and Energy Star’s portfolio manager provide context for commercial buildings in the private sector. A similar reference was needed for Army buildings that are often quite different in design and function to commercial buildings. The US Army Corps of Engineers (USACE) in collaboration with Pacific Northwest National Laboratory (PNNL) developed baseline models for five common Army building types. The focus of this initial study was on newly constructed buildings (post 2008) that were designed using standardized design documents created in response to EPACT 2005. Using metered data from these newly constructed buildings, EnergyPlus whole building energy models of the standard designs were calibrated. These models were then reverted to a Standard 90.1-2007 baseline using rules established by Appendix G of Standard 90.1-2007. The Standard 90.1-2007 baseline is important because it allows comparison of the performance of buildings to the EPACT 2005 requirement of building performance being 30% better than Standard 90.1-2007. This paper describes the methodology behind the creation of the baseline models and also describes how these models will be used to output results for MDMS. The methodology starts with processing of raw data from MDMS, choosing data for calibration, performing model calibration and reverting calibrated models to the Standard 90.1-2007 baseline. By using calibrated models to generate the baseline, the actual operating of buildings for a given location is captured in the models. The paper will describe the advantages and disadvantages of the approach, and will summarize ways in which baseline models will be used and how they will benefit MDMS and its users.

Both the current and future buildings will have very well-insulated building envelopes heated mostly by internal heat gains from occupants and household electricity. The occupant related energy uses, household electricity and domestic hot water heating, will have a large impact on the energy performance. In low-energy buildings the heating of domestic hot water is in the same order of magnitude as the energy for space heating. Knowledge of occupancy levels is critical for prediction and verification as well as optimization of various demand controlled systems. This article presents how household electricity and domestic hot water use varies due to occupancy level. Occupancy level, household electricity and domestic hot water were measured in 79 apartments during 12 days per apartment. Occupancy level was measured by electronic diaries in which those living in and visiting the apartment marked their attendance by pressing buttons when entering and leaving the apartment. Household electricity and domestic hot water were measured hourly while the diaries recorded data every second. The result shows that there are relatively weak correlations between occupancy level and the studied energy uses demonstrated by a large variation in both household electricity and hot water use at the same occupancy level. Household electricity has a stronger correlation to the occupancy level during the day compared to the occupancy level during the night, where the level during the night is assumed to describe how many people are living in the apartment. The hot water has a stronger correlation to the occupancy level during the night compared to the occupancy level during the day. One possible explanation is that larger quantities of domestic hot water could be due to showering in the morning, which would depend on how many people spent the night in the apartment while household electricity could depend more on how much of the day there are people in the apartment. The result shows that the average occupancy level during the day or the number of people living in an apartment describes only a small part of the use of household electricity and domestic hot water. Probably there is considerable individual differences in how people use electricity and hot water, which leads to a need to describe the variation by statistical distributions.

The importance of follow ups of buildings energy use during operation to optimize the performance and identify deviations and errors has attracted attention during recent years. As buildings are becoming more energy efficient with a better exterior envelope, household electricity is becoming an increasingly important part of the total heating energy and, in turn, the optimization of the thermal performance of the exterior envelope. Variations between different years can be thought to have a significant impact. Unfortunately, it seems that enough knowledge, studies and data that describe how the household electricity varies between different years are missing. A low household electricity use during one year might imply a certain hvac and building design while a high household electricity use might imply another optimal design, and this may vary over time. This might also in part explain reported gaps between calculated and measured energy use. To supply the industry with reference data on typical variations during time and between different users, there is a need for several consecutive years of measurements of household electricity in the same apartments in a large enough sample of apartments to obtain good statistics. Household electricity has been measured in 539 apartments in 25 multi-family buildings in Sweden during six years. This paper presents statistics and characteristics of the variation between years and between different apartments and buildings. The results show large differences between years, regarding both apartments and buildings, and a conclusion is that this should be considered booth during design and operation.