Calibration Procedure for Energy Performance Simulation of a Commercial Building

Abstract

Calibration of an energy simulation with actual data has generally been considered too difficult to be part of the energy audit procedure. The purpose of this paper is to develop a systematic method using a “base load analysis approach” to calibrate a building energy performance model with a combination of monthly utility billing data and sub-metered data such as is commonly available in large buildings in Korea. The calibration procedure was specifically developed to be suitable for use in both the audit and savings determination procedure within a retrofit process. The procedure has been visualized using a logical flow chart and demonstrated using the simulation of a 26-story commercial building located in Seoul as a case study. The results indicate that the approach developed provided a reliable and accurate simulation of the monthly and annual building energy requirements of the case study building.

Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration

“…Therefore, energy models are difficult to calibrate for non-working hours without considering overtime as an important input. In current building simulations, the internal heat gains and HVAC operation schedules are usually deterministic schedules based on a typical weekday, weekend or holiday, either from measurement or design practice [22][23][24]. These schedules do not realistically represent overtime schedules due to their stochastic nature, and because of simplistic and idealistic data inputs that are unrepresentative of actual occupancy [25].…”

A methodology for building energy modelling and calibration in warm climates

“…The question raised is how much e ort and resources are necessary to produce a satisfactory model. The attempt of answer is provided with a calibration method [4]. This is the starting point for this paper, which presents a modelling and calibrating method with successive increasing levels of complexity and their impact on the results.…”

Study on Auto-DR and pre-cooling of commercial buildings with thermal mass in California

“…Haberl et al [18] and Kreider et al [19,20] used statistical mean bias error (MBE) and coefficient of variation of the root mean squared error (CV(RMSE)) to evaluate the accuracy of the predicted results of the simulation model. Pan et al [14] and Yoon et al [11] also applied the criteria as shown in Table 3 into the calibration procedure for energy simulation of commercial buildings. Several calibration approaches and guidelines have been developed that use statistical techniques as part of the calibration process [10,21].…”

“…Based on the calibrated baseline models, various demand response strategies were simulated to determine how to discharge thermal mass efficiently and smoothly with no rebound in electricity demand. [11] developed a systematic calibration method using a "base load analysis approach" that calibrates a building energy performance model with a combination of monthly utility billing data and sub-metered data. The results of the case study indicated that the approach provided a reliable and accurate simulation of the monthly and annual building energy requirements.…”