2 An Hourly Hybrid Multivariate Change Point Inverse Model Using Short-Term Monitored Data for Annual Prediction of Building Energy Performance: Results and Analysis

Mitch Paulus, P.E., Texas A&M University
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.

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