A Comparative Analysis of Predicting Energy Savings from Energy Service Projects
Implementation of energy service projects continue to increase as building owners are faced with higher utility bills, rigorous environmental regulations, and shrinking capital allocation for such projects. Different techniques and guidelines are available to select and quantify energy service projects. These methods range from various Technical reference manuals (TRMs) developed by state agencies in conjunction with energy consultants to standard protocols developed by energy professional organizations. All of these methods require gathering or estimating representative input variables, with various approaches to data collection that vary from stipulation to measurement-based values. The methods to quantify the savings range widely from engineering algorithms to as-built calibrated whole-building energy simulation models.
In this study, a comparison is made between the engineering algorithms supported by many TRMs and a more accurate as-built calibrated whole-building energy simulation model. The methods to performing the comparison included identifying industry methods through literature reviews, expert interviews, a desk audit of a typical utility assessment report, and constructing an as-built calibrated whole-building energy simulation model of a well-instrumented, large office building near the Texas A&M University campus. Lighting and lighting control energy conservation measures (ECMs) were selected to demonstrate the methodology. As part of the process of constructing the simulation model, a data collection protocol was also created. The data collection protocol included gathering building and site specific information including sub-hourly measured energy consumption data and measured climatic data for the baseline year.
The study results showed that the industry methods of quantifying the total energy savings for lighting and lighting control ECMs were consistently under-reporting the savings as compared to the calibrated as-built whole-building energy simulation model. In particular, the breakdown of savings was inconsistent between the various industry methods that are currently in use. The differences were perceived to be location specific and weather driven and also included agreements with the local utility companies to quantify the demand savings. Finally, the study results also indicated that the current industry methods could be significantly improved by measuring the occupancy schedule and indoor temperature.