Effectiveness of Automatic and Manual Calibration of an Office Building Energy Model

Abstract

Featured Application: This work could contribute to evidence strengths and weaknesses of manual and automatic calibration of building dynamic simulation models leading at improving the quality of building retrofit solutions investigation.Abstract: Energy reduction can benefit from the improvement of energy efficiency in buildings. For this purpose, simulation models can be used both as diagnostic and prognostic tools, reproducing the behaviour of the real building as accurately as possible. High modelling accuracy can be achieved only through calibration. Two approaches can be adopted-manual or automatic. Manual calibration consists of an iterative trial and error procedure that requires high skill and expertise of the modeler. Automatic calibration relies on mathematical and statistical methods that mostly use optimization algorithms to minimize the difference between measured and simulated data. This paper aims to compare a manual calibration procedure with an automatic calibration method developed by the authors, coupling dynamic simulation, sensitivity analysis and automatic optimization using IDA ICE, Matlab and GenOpt respectively. Differences, advantages and disadvantages are evidenced applying both methods to a dynamic simulation model of a real office building in Rome, Italy. Although both methods require high expertise from operators and showed good results in terms of accuracy, automatic calibration presents better performance and consistently helps with speeding up the procedure. consumption of a building and consists of the construction of a virtual model of the building that has to reproduce the behaviour of the real building as accurately as possible. The reduction of the energy consumption can be pursued if the energy distribution throughout the building is known and understood. Building modelling can then be used both as a diagnostic tool, to better understand the physics laws of the building, and as a prognostic tool to predict the building's behaviour once the laws are known. Building energy performance simulation (BEPS) can be achieved using data driven (black box) or law driven (white box) models. Considering the white box approach, many BEPS tools are available for example, TRNSYS, Energy Plus, ESPr, and for the most popular, accurate capabilities evaluations and comparisons have been made [10]. Retrofit projects and operations management for buildings require accurate modelling that can be achieved through calibration. Calibration consists of an iterative process where model input is changed to reconcile the model output with the measured data. Although this process can be considered essential for having reliable results, still few practitioners are capable to perform it. Typical variables that are involved in the calibration process are mainly energy consumption [11][12][13][14] and indoor air temperature [11,15,16], for which simulated values are compared to measured ones on a specific time scale. Usually calibration at a small time scale (hourly of sub-hourly) results more difficult and...

Energy Efficiency in Buildings and Innovative Materials for Building Construction

“…Papers [1][2][3] are focused on simulation techniques for the evaluation of the energy performance of buildings. For example, Sabina Jordan [1] and her colleagues conducted a parametric study of the influence of the occupant behavior on the energy performance of an office building provided with radiant ceiling panels for heating and cooling.…”

“…They used TRNSYS, and they concluded that specific human activities influence the annual energy use a small amount and their effects often counteract. Cristina Cornaro and her colleagues [2] made a comparison of two different procedures for energy dynamic model calibration-manual and automatic-using the IDA ICE tool. They built up an automatic calibration procedure and compared it to standard manual calibration for an office building located in Rome, Italy.…”

Calibrating the Dynamic Energy Simulation Model for an Existing Building: Lessons Learned from a Collective Exercise

“…For this reason, the process is often streamlined by the modeler's experience [6], or, in a more objective way, by a preliminary sensitivity analysis identifying the most influential parameters. In an automatic calibration, the analyst identifies the uncertain parameters and their variation, while an automated process based on mathematical or statistical methods is used to perform the calibration [7]. Optimization methods are often adopted, so that one or more objective functions are set and the solutions of the calibration problem correspond to the local minima obtained by an optimization program coupled with the BES tool.…”

Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring

“…For example, Hong, Kim, Jeong, Lee and Ji [33] calibrated a school building using a genetic algorithm to find global optimal solutions; and Sanyal, New and Edwards [34] automatically calibrated a BEM using trained machine agents from a large set of parametric simulations through machine learning, exploiting supercomputing resources. A general disadvantage of optimization methods is the dependency of the calibration procedure on the optimization settings [35].Difficulties may arise in selecting optimization hyperparameters settings, which can lead to local minima problems [32].…”