Evaluating different levels of information on the calibration of building energy simulation models

A poorly calibrated model undermines confidence in the effectiveness of building energy simulation, impeding the widespread application of advanced energy conservation measures (ECMs). Striking a balance between information-gathering efforts and achieving sufficient model credibility is crucial but often obscured by ambiguities. To address this gap, we model and calibrate a test bed with different levels of information (LOI). Beginning with an initial model based on building geometry (LOI 1), we progressively introduce additional information, including nameplate information (LOI 2), envelope conductivity (LOI 3), zone infiltration rate (LOI 4), AHU fan power (LOI 5), and HVAC data (LOI 6). The models are evaluated for accuracy, consistency, and the robustness of their predictions. Our results indicate that adding more information for calibration leads to improved data fit. However, this improvement is not uniform across all observed outputs due to identifiability issues. Furthermore, for energy-saving analysis, adding more information can significantly affect the projected energy savings by up to two times. Nevertheless, for ECM ranking, models that did not meet ASHRAE 14 accuracy thresholds can yield correct retrofit decisions. These findings underscore equifinality in modeling complex building systems. Clearly, predictive accuracy is not synonymous with model credibility. Therefore, to balance efforts in information-gathering and model reliability, it is crucial to (1) determine the minimum level of information required for calibration compatible with its intended purpose and (2) calibrate models with information closely linked to all outputs of interest, particularly when simultaneous accuracy for multiple outputs is necessary.

A cost effective approach to design of energy efficient residential buildings

The objective of this study is to identify cost-optimal efficiency packages at several levels of building energy savings.A two-story residential building located in Jordan is selected as a case study.DesignBuilder software is used to predict the annual energy usage of a twostory residence in Irbid,Jordan.Real-time experimental data from a single isolated controlled room was used to verify the proposed model.In addition to energy analysis,the economic,environmental,and social benefits of the proposed design have been investigated.The sequential search optimization approach is used to estimate the minimum cost of the building while considering various design scenarios.In addition,the impact of various energy conservation techniques on residential buildings is assessed,and the payback period for each program is calculated.Ultimately,the optimal combination of design to achieve energy efficiency measures has been identified in several climate regions.The simulations results predict that the annual electricity consumption can be reduced up to 50%if the proper combinations of energy conservation measures are selected at the lowest cost.The payback period is 9.3 years.Finally,energy efficiency measures can lead to a total of 9470 jobs/year job opportunities.The study provide practical framework to link between energy performance criteria and economic goals of building.Linking the energy performance requirements to economic targets provides guidelines for homeowners,contractors,and policymakers for making a suitable decision regarding the retrofitting of existing residential buildings.The study focuses on developing new methodologies that support minimizing costs during a building’s lifecycle while maximizing environmental benefits which can not be identified by a series of parametric analyses using individual energy-efficient measures.