Calibrated simulation is the process of using a building simulation program for an existing building and “tuning” or calibrating the various inputs to the program so that predictions match closely with observed energy use. Historically, the calibration process has been an art form that inevitably relies on user knowledge, past experience, statistical expertise, engineering judgment, and an abundance of trial and error. Unfortunately, despite widespread interest in the professional community, no consensus guidelines have been published on how to perform a calibration using detailed simulation programs. This research project was initiated with the intention to cull the best tools, techniques, approaches, and procedures from the existing body of research and develop a coherent and systematic calibration methodology that includes both parameter estimation and the determination of the uncertainty in the calibrated simulation. A general methodology of calibrating detailed simulation programs to performance data is proposed, which we deem to be methodical, rational, robust, and computationally efficient while being flexible enough to satisfy different users with different personal preferences and biases. The methodology involves various concepts and approaches borrowed from allied scientific disciplines that are also reviewed in this paper. The methodology essentially consists of five parts: (1) identify a building energy program that has the ability to simulate the types of building elements and systems present and set up the simulation input file to be as realistic as possible; (2) depending on the building type, heuristically define a set of influential parameters and schedules that have simple and clear correspondence to specific and easy-to-identify inputs to the simulation program, along with their best-guess estimates and their range of variation; (3) perform a coarse grid search wherein the heuristically defined influential parameters are subject to a Monte Carlo simulation involving thousands of simulation trials from which a small set of promising parameter vector solutions can be identified by filtering, the strong and weak parameters can be identified, and narrower bounds of variability of the strong parameters can be defined; (4) perform a guided grid search to further refine the promising parameter vector solutions; and (5) use this small set of solutions (as opposed to a single calibrated solution) to make predictions about intended changes to the building and its systems, and determine the prediction uncertainty of the entire calibration process. A companion paper (Reddy et al. 2007) will present the results of applying this calibration methodology to two synthetic office buildings and one actual office building.
ASJC Scopus subject areas
- Building and Construction