Energy Modelling and Automated Calibrations of Ancient Building Simulations: A Case Study of a School in the Northwest of Spain

Abstract

Abstract:In the present paper, the energy performance of buildings forming a school centre in the northwest of Spain was analyzed using a transient simulation of the energy model of the school, which was developed with TRNSYS, a software of proven reliability in the field of thermal simulations. A deterministic calibration approach was applied to the initial building model to adjust the predictions to the actual performance of the school, data acquired during the temperature measurement campaign. The buildings under study were in deteriorated conditions due to poor maintenance over the years, presenting a big challenge for modelling and simulating it in a reliable way. The results showed that the proposed methodology is successful for obtaining calibrated thermal models of these types of damaged buildings, as the metrics employed to verify the final error showed a reduced normalized mean bias error (NMBE) of 2.73%. It was verified that a decrease of approximately 60% in NMBE and 17% in the coefficient of variation of the root mean square error (CV(RMSE)) was achieved due to the calibration process. Subsequent steps were performed with the aid of new software, which was developed under a European project that enabled the automated calibration of the simulations.

Energy Modelling and Calibration of Building Simulations: A Case Study of a Domestic Building with Natural Ventilation

“…Coefficient of variation root mean square error, CV(RMSE), and normalized mean bias error (NMBE) for Model 1 were 5.25% and −0.38, respectively (See Table 3). The indexes obtained were within a range of values that were considered acceptable and similar to those obtained in previous works using TRNSYS [19] and the ASHRAE Guideline 14 criteria to validate simulations of an entire building a NMBE and a CV(RMSE) should be lower than 10% and 30%, respectively, when hourly data are compared [19]. Figure 5 shows the indoor air temperatures simulated with Model 1 and 2 versus the experimental data for the building during the holiday period (Period 5, see Table 2).…”

“…In the assessment field, software programs for simulating the building energy performance have become essential tools, for example, Energy Plus, DOE-2, TRNSYS (TRaNsient SYstem Simulation program), or ESP-r (Environmental Systems Performance-Research) [1,[9][10][11][12][13][14][18][19][20][21], which typically assume constant air infiltration rates [12]. A number of assumptions need to be made and these can vary between buildings, hence model calibration is an important factor [19]. In the calibration process, a model is developed through observed data recorded from the real system and is adjusted to obtain predicted results in order to align with either energy demand or indoor temperature and it is systematically evaluated, the most common method being from ASHRAE [22].…”

Analysis of Accuracy Determination of the Seasonal Heat Demand in Buildings Based on Short Measurement Periods

“…The buildings were described in the programs as macro-scale models, represented as a series of idealized zones with the constant parameters of the air within the limits of the entire zone. The models created in the programs possessed a well-imaged reality, which has been confirmed in many publications, including the authors of this article [26][27][28][29][30][31]. Due to the similar room properties (temperature, usage), some modifications have been made by combining several rooms into a single zone.…”

Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods

“…Typically, an initial model is created manually, parameters are selected for tuning and boundary limits are manually selected for each parameter. The algorithm then selects the final values of these parameters through an iterative error minimization process [8,32]. The difference in time taken to manually or automatically calibrate models has been shown in some instances to be very substantial [26,32].…”