AUTOTUNE E+ BUILDING ENERGY MODELS

Abstract

This paper introduces a novel “Autotune ” methodol-ogy under development for calibrating building energy models (BEM). It is aimed at developing an automated BEM tuning methodology that enables models to repro-duce measured data such as utility bills, sub-meter, and/or sensor data accurately and robustly by selecting best-match E+ input parameters in a systematic, automated, and repeatable fashion. The approach is applicable to a building retrofit scenario and aims to quantify the trade-offs between tuning accuracy and the minimal amount of “ground truth ” data required to calibrate the model. Au-totune will use a suite of machine-learning algorithms de-veloped and run on supercomputers to generate calibra-tion functions. Specifically, the project will begin with a de-tuned model and then perform Monte Carlo simula-tions on the model by perturbing the “uncertain ” parame-ters within permitted ranges. Machine learning algorithms will then extract minimal perturbation combinations that result in modeled results that most closely track sensor data. A large database of parametric EnergyPlus (E+) simulations has been made publicly available. Autotune is currently being applied to a heavily instrumented residen-tial building as well as three light commercial buildings in which a “de-tuned ” model is autotuned using faux sensor data from the corresponding target E+ model.