Potential Operation and Maintenance (O&M) Savings in the John Sealy North Building at UTMB

The LoanSTAR Monitoring and Analysis Group, Energy Systems Laboratory at Texas A&M University, was requested by University of Texas Medical Branch at Galveston to investigate O&M measures in their five LoanSTAR program buildings. This report describes the suggested O&Ms in John Sealy North Building, a surgical building of 54,494 ft2,which currently spends 502,100 per year on electricity, steam and chilled water. The suggested O&Ms include optimizing the outside air treatment cold deck reset schedule, the cold deck reset schedule and the hot deck reset schedule. These optimized HVAC operation schedules were determined using an analysis involving a simplified HVAC model, which was calibrated against daily data measured by the LoanSTAR program. It is estimated that annual savings of 67,000, or 13% of the annual costs, can be realized using the optimized operation schedules which can be implemented without additional costs. Our analysis indicates that the room comfort levels will not be degraded by these measures

Bias in Predicting Annual Energy Use in Commercial Buildings with Regression Models Developed from Short Data Sets

Issues relating to bias in regression models identified from short data sets are discussed in this paper. First, the physical reasons for the differences between the predictions of the annual energy consumption based on a short data set model and on a long data set model are discussed. Then, the errors associated with the multiple linear regression model are evaluated when applied to short data sets of monitored data from large commercial buildings in Texas. The analysis shows that the seasonal variation of the outdoor dry-bulb and dew-point temperature causes significant errors in the models developed from short data sets. The MBE (mean bias error) from models based on short data sets (one month) varied from +40% to -15%, which is significant. Hence, due care must be exercised when applying the regression modeling approach in such cases.An empirical or regression modeling approach is simple to develop and easy to use compared to use of detailed hourly simulations. Therefore, regression analysis has become a widely used tool in the determination of annual energy savings accruing from energy conserving retrofits. The regression modeling approach is accurate and reliable if several months of data (more than six months) are used to develop the model. If such is not the case, the regression models can, unfortunately, lead to significant errors in the prediction of the annual energy consumption

Development of a Toolkit for Calculating Linear, Change–Point Linear and Multiple–Linear Inverse Building Energy Analysis Models, ASHRAE Research Project 1050-RP, Detailed Test Results

This is the detailed test report for the ASHRAE 1050-RP project. This report presents the detailed results of the testing of IMT (Inverse Modeling Toolkit). Two kinds of testing were performed, bounds testing and accuracy testing. The bounds testing is performed in order to identify what types of data sets the IMT program can model reliably. A variety of data sets were used to test the limits of the program: 1) Data sets with as few as two and as many as 9,000 data points, 2) Data sets with very large and very small numbers, 3) Data set with a variety of slopes, and 4) Data sets with tightly packed and widely scattered observations. In terms of accuracy test, 1P, 2P and MVR models were benchmarked against the statistical software SAS (SAS Institute Inc., 2001). The change-point model results (3P and 4P) were compared to those calculated by the data analysis software EModel (Kissock et al., 1996). Finally, the IMT's HDD and CDD models were compared to PRISM HO and CO models (Fels, M. et al., 1986)

Disaggregating Cooling Energy Use of Commercial Buildings Into Sensible and Latent Fractions From Whole-Building Monitored Data: Methodology and Advantages

In hot and humid climates, where summers are both warm and humid, the latent cooling can be a significant portion of the total cooling load (as much as 40%). Typically the monitored data only includes whole-building heating and cooling energy use and total electric consumption. A method to disaggregate the latent cooling energy use from the measured whole-building heating and cooling energy use would be of particular interest. This paper presents such a method and discusses its benefits. It is shown that the overall heat transfer coefficient including the conduction, infiltration, and ventilation effects of a building, can be evaluated. Subsequently this enables the disaggregation of the total cooling energy use into sensible and latent cooling fractions. The benefits of such a method include: (i) better understanding of the sensible and latent fractions in the total cooling energy use of a building, and (ii) better regression models for energy analysis. In addition to the whole-building cooling and heating energy use and the ambient conditions, the required system parameters include: (i) cold deck supply temperature, (ii) hot deck supply temperature, (iii) mixed air temperature or ventilation rate, (iv) internal gains, and (v) total mass flow rate of the dual duct constant volume system. If continuous measurements of the system parameters are not available, then one-time measurements may be used to disaggregate the latent cooling energy use

Compilation of Diversity Factors and Schedules for Energy and Cooling Load Calculations, ASHRAE Research Project 1093, Phase III Draft Report, Compilation of Diversity Factors and Load Shapes

During this phase of the project, we finalized the daytyping method to be followed, and started processing the data sets previously approved by the PMSC. So far, we processed a total of 23 buildings (ESL). The final product will include typical load shapes and diversity factors from 27 Office Buildings monitored by ESL and 9 Office Buildings provided by LBNL (Energy-Edge Buildings). If time allows, we will process 28 additional buildings provided to us by PNNL. These additional buildings were monitored under the ELCAP project. We prepared typical templates (with Microsoft Word) to describe each building along with the corresponding results of the analysis. Mr. Micheal Witte, from Gard Analytics, helped us in writing the BLAST input files, and he also automated the procedure of copying the results from EXCEL to the WORD templates. Table 4 shows the final set of buildings that are currently analysed.This is a draft of the Final Report in the ASHRAE RP-1093 project that, first summarizes the work completed during the scheduled Phase I and Phase II (presented to the PMSC in Seattle - June 1999, and Dallas February 2000), and reports on the progress during the scheduled Phase III effort (Table 1). It should be noted that the PMSC approved a one-year extension after the May-2000-Completion-date noted in Table 1. Tables 2 and 3 show the buildings that were approved by the PMSC in previous meetings

Data Visualization for Quality-Check Purposes of Monitored Electricity Consumption in All Office Buildings in the ESL Database

A total of 44 "pure" Office buildings in the database at the Energy Systems Laboratory, was identified. Other office buildings comprising classrooms and laboratories - typical case of educational buildings, for instance the University of Texas - Austin, and TAMU buildings) - are not included in this report. The report is organized in a specific format; for each site, we included: (1) the contact name for the site, the facility's name, square footage, the starting date of the monitoring (information obtained with the "listsite" commend in the database, (2) the site description that is typically included in the Annual Energy Consumption Reports (AECR) and the Monthly Energy Consumption Reports (MECR), (3) the cover page of each site in the MECR showing savings and comments, (4) a sample of the monthly energy consumption for each site (from the MECR), including time series, scatter plots, and 3-D plots of the energy use, which give a preliminary insight into the energy performance of each building, (5) long-term time series (the whole available data) of the Whole Building Electricity (WBE) consumption, and the Motor Control Center (MCC) consumption whenever available, and (6) short-term time series (one year) of the Whole Building Electricity (WBE) consumption, and the Motor Control Center (MCC) consumption whenever available. The one-year hourly time series, is usually sufficient for most of the application (baselining, saving calculation, establishment of EUI's, development of Typical Load Shapes, etc).This report comprises an effort to visualize the monitored electricity consumption in all office buildings (not including the office buildings comprising other functions as classrooms and laboratories, for instance) in the ESL database. This data visualization, basically long-term and short-term time series plots serves as a preliminary quality check of the data available. A preliminary inspection of the data was performed, by viewing the channels to provide a clear identification of creep, missing data gaps, turned-off periods, and sudden big changes that suggest changes in the building operation or an addition to the building

A Field Study on Residential Air Conditioning Peak Loads During Summer in College Station, Texas

The measured data and various analysis approaches described were able to qualitatively reveal whether the AC was oversized or not but exact quantification was not possible. The same held true in terms of being able to identify the presence of human behavior on thermostat operation (and thus, on whole-house electricity peaks). Consequently, we were unable to quantitatively determine the amount of peak shaving potential in these houses.Severe capacity problems are experienced by electric utilities during hot summer afternoons. Several studies have found that, in large part, electric peak loads can be attributed to residential airconditioning use. This air-conditioning peak depends primarily on two factors: (i) the manner in which the homeowner operates his air-conditioner during the hot summer afternoons, and (ii) the amount by which the air-conditioner has been over-designed. Whole-house and air-conditioner electricity use data at 15 minute time intervals have been gathered and analyzed for 8 residences during the summer of 1991, six of which had passed the College Station Good Cents tests. Indoor air temperatures were measured by a mechanical chart recorder, while a weather station located on the main campus of Texas A&M university provided the necessary climatic data, especially ambient temperature, relative humidity and solar radiation. The data were analysed to determine the extent to which air-conditioning over-sizing and homeowner intervention contributes to peak electricity use for newer houses in College Station, Texas

Compilation of Diversity Factors and Schedules for Energy and Cooling Load Calculations, ASHRAE Research Project 1093-RP, Final Report

In this report a day-typing method that uses a percentile analysis is described. In the percentile analysis the 50th percentile was used to calculate the diversity factors and the typical hourly load shapes; other statistics are reported as well, including: the mean, the mean plus or minus one standard deviation, and the 10th, 25th, 75th, and 90th percentiles. This percentile calculation has been codified into a MS Excel spreadsheet that can be used for analyzing up to one year of hourly data in the proper format. A table of comparative EUIs and a summary of the results have also been included to facilitate easier comparison of the profiles. In general the method divides the year into weekday and weekends; allowing the user to include or remove holidays as needed. The spreadsheet then produces three forms of output; a) tabular output describing the statistics of the diversity profiles that are developed, b) graphical output of the diversity profiles, and c) ready-to-use input files for the DOE-2, BLAST and EnergyPlus simulation programs. Electronic copies of all the diversity factor profiles in this report are provided in the accompanying CDROM as part of the MS Word file.This is the final report for the ASHRAE 1093-RP project. This report presents the method used to derive the diversity factors and typical load shapes of lighting and receptacle loads in office buildings. In this report the results of the application of the diversity factor calculations are applied to the data collected for this project. The buildings analyzed for this report consisted of office buildings monitored by the ESL, and office buildings provided by the LBNL

Summary of UTMB O&M Project: Energy Conservation Potential in Five Buildings

This report is a summary of five reports (references 1 to 5) which provided detailed descriptions of an O&M investigation of the following five buildings on the UTMB campus: 1) John Sealy North Building(JSN); 2) Clinical Science Building(CSB); 3) Basic Science Building(BSB); 4)Moody Library Building(MLB); and 5) John Sealy South Building(JSS). In these five buildings, the soft tune up is the major O&M measure identified. This report briefly describes the buildings, summarizes the methodology used and the O&M measures identified for each building, presents simulated energy savings, measured savings and conclusions

Potential Operation and Maintenance (O&M) Savings in the John Sealy North Building at UTMB

The LoanSTAR Monitoring and Analysis Group, Energy Systems Laboratory at Texas A&M University, was requested by University of Texas Medical Branch at Galveston to investigate O&M measures in their five LoanSTAR program buildings. This report describes the suggested O&Ms in John Sealy North Building, a surgical building of 54,494 ft2,which currently spends 502,100 per year on electricity, steam and chilled water. The suggested O&Ms include optimizing the outside air treatment cold deck reset schedule, the cold deck reset schedule and the hot deck reset schedule. These optimized HVAC operation schedules were determined using an analysis involving a simplified HVAC model, which was calibrated against daily data measured by the LoanSTAR program. It is estimated that annual savings of 67,000, or 13% of the annual costs, can be realized using the optimized operation schedules which can be implemented without additional costs. Our analysis indicates that the room comfort levels will not be degraded by these measures