Energy model calibration in an office building by an optimization-based method

In the last years, building energy consumption minimization has come to be an important issue for designers also architects. Different building energy simulation (BES) tools were applied. These programs are efficient to estimate building energy demands and quicken the malfunction assessment. Many of the provided energy simulation tools cannot precisely forecast building energy operation because of numerous interacting variables. To lessen considerable discrepancies between the actual time data measurements and the simulation achievements, an optimization-based calibration method is presented in this study. Therefore, to minimize this error an optimization algorithm called Slime Mold Optimization (SMO) algorithm is utilized also the energy simulation model. A case study, an office building placed in Dubai, the United Arab Emirates (UAE) in a humid and hot climate region is selected to be modeled and adjusted to show the accuracy of the applied procedure. This case has five floors with 3610 m(2) footprint. The building uses only electrical power as the major energy source For the total dataset duration (n = 3216), the MBE of an hour for the calibrated model is equal to 3.24 percent with the CV (RMSE) equal to 11.3 percent. The statistical evaluation has been used for analyzing the precision of the achievements. Based on the results, the procedure is reliable. (C) 2021 Published by Elsevier Ltd.

Automated summary

Building energy consumption minimization is a crucial issue for designers and architects, with simulation tools used to estimate demands and accelerate fault assessment. However, inaccuracies may arise due to interacting variables. An optimization-based calibration method, utilizing the Slime Mold Optimization algorithm, is proposed in this study. Case studies and statistical evaluations demonstrate the reliability of the procedure.