The increasing popularity of building energy modeling (BEM) yields growing awareness of BEM to a variety of professionals. With exposure to larger audiences of various technical nature, energy modeling remains under increasing scrutiny. This scrutiny extends to all energy models, including yours.

Critics, all too often, cite discrepancies between a given BEM's projected energy cost and the reported cost of the existing building.Thus, the perceived validity of the energy-model is tied to its accuracy relative to actual utility bill data. This accuracy is most easily determined in existing building energy retrofits, where one can easily cross-reference existing utility bills with the energy model’s results.

The industry, the client, and the building simulator benefit when a model is properly calibrated with the building’s existing utility data. Given the thousands of inputs available in an energy model, building simulators often struggle with calibration of the energy-model, which illustrates the need for a systematic approach to calibrate the energy model.

While there are complex tools available, this course covers a step-wise approach to calibrating a BEM to an existing building.The methodology starts by specifying the variables that require definition prior to simulation. It is common that simulation data requires estimation, and therefore the simulation data is cross referenced with similar building types from an NREL database, which identifies projected errors and systematically identifies the variables a simulator should prioritize when calibrating the model. Since many variables, such as interior lighting, have a consistent impact on cooling, heating, fan operation, and other inputs, the process incorporates pattern recognition to help identify potentially inaccurate inputs.

The chief parameters evaluated are the annual energy consumption and the energy consumption in the cooling and heating months. The modeled energy consumption is compared to the actual energy consumption and accuracy is determined by the coefficient of variation. When coupled with a database of existing building, this data provides specific feedback which saves the modeler time and iterations.

Learn more about the process of calibrating energy models.