An inquiry into the capabilities of baseline building energy modelling approaches to estimate energy savings

Abstract

While baseline energy modelling approaches for buildings are progressing to meet the demand for measurement and verification (M&V) of savings of an energy performance contract, more robust and insightful modelling approaches are needed. This study compares four baseline modelling approaches (e.g., change point, nonlinear autoregressive exogenous models (NARX), Gaussian process, and ensembles of trees) in terms of predictive accuracy and operational insights. The approaches were selected based on preliminary study and employed on pre- and post-intervention data gathered from twelve commercial buildings in Ottawa, Canada. Changes in the post-intervention energy use were estimated by comparing the predicted data with measured data for the individual buildings. The known primary intervention in these buildings was implementing commercially available smart building technologies that identify and address operational suboptimalities. A deeper analysis of one building’s automation system data indicates that energy savings were largely due to simple corrective actions such as eliminating the concurrent operation of heating and cooling coils of an air handling unit. The results show that saving estimates are largely affected by the choice of baseline energy modelling approach. NARX outperformed all other modelling approaches regarding accuracy and data fitting capability. On the contrary, the change point modelling approach offered insights into the operational performance of the buildings.