RP-1404 developed analysis methodologies by which the time period for field monitoring of energy use in buildings can be reduced to less than a whole year while satisfying preset accuracy levels of annual energy performance verification. The seminar presents the methodology of investigating the capabilities and the limits of hybrid inverse models developed from the shortest monitoring periods possible for a reliable and accurate long-term energy performance prediction in large commercial buildings. Such methodologies would be of great benefit to high performance buildings, and to Energy Service Companies who need a more cost-effective and acceptable alternative to year-long monitoring.

The hourly hybrid multivariate change point approach aimed at predicting building energy consumption by combining a short-term data set of monitored energy consumption, weather variables and internal loads with at least one year of recent utility bills. Two weeks of monitoring of hourly data in many cases, along with utility history representing the long-term data, were found to be sufficient for estimating long-term energy consumption. This seminar shows the hourly time scale results of RP-1404, along with an analysis that provides recommendations and guidance to energy modelers in their use of short-term monitoring for long-term prediction of building energy performance.

This presentation deals with two simple inverse modeling methods and data monitoring protocols which can be used to identify statistical models that would result in accurate daily energy use predictions. The Dry Bulb Temperature Analysis (DBTA) method only requires measuring dry-bulb ambient temperature for 2-3 months but the monitoring period and length have to be selected judiciously. The Hybrid Inverse Model using daily data (HIM-D) only requires about one month of monitoring and utility bills. The model combines information from recent year-long utility bill data along with a few weeks of monitored building energy use, weather variables and internal loads.