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Digital twin model calibration of HVAC system using adaptive domain Nelder-Mead method
Energy and Buildings ( IF 6.6 ) Pub Date : 2025-01-18 , DOI: 10.1016/j.enbuild.2025.115340
Ga-Yeong Lee, Yoojeong Noh, Young-Jin Kang, Nuri Kim, Noma Park, Been Oh, Gyungmin Choi

Digital twin control enhances HVAC systems by enabling real-time monitoring, energy optimization, and maintenance for improved performance and energy efficiency. To perform digital twin control effectively, it is necessary to have an accurate simulation model that can predict the performance of HVAC systems. However, simulation models have limitations in replicating real operating conditions, requiring calibration using actual measurement data. HVAC systems vary in operating environments, necessitating on-site embedded systems for customized operational optimization. However, existing research utilizing physics-based simulation models and metaheuristic algorithms incurs high computational costs, making practical implementation challenging. In this study, we developed the adaptive domain Nelder-Mead (ADNM) method, which optimizes calibration coefficients of the simulation model to minimize the error between predicted values and measured data in a short computation time. ADNM constructs an initial simplex using the Sobol sequence and previous optimal solutions, enabling robust minimization of the objective function even with changing operating conditions. The proposed method, validated with real HVAC data, reduced temperature RMSE by 60–70 % compared to the traditional NM algorithm and demonstrated up to 10–60 % lower temperature error than Bayesian optimization (BO) at similar computational times. Although genetic algorithm (GA) achieved lower errors, its computational time was over 3 to 6 times higher, highlighting the proposed method’s superior efficiency and accuracy.

中文翻译:


使用自适应域 Nelder-Mead 方法对 HVAC 系统进行数字孪生模型标定



数字孪生控制通过实现实时监控、能源优化和维护来提高性能和能源效率,从而增强 HVAC 系统。为了有效地执行数字孪生控制,必须有一个准确的仿真模型来预测 HVAC 系统的性能。然而,仿真模型在复制实际操作条件方面存在局限性,需要使用实际测量数据进行校准。HVAC 系统因操作环境而异,因此需要现场嵌入式系统进行定制的操作优化。然而,利用基于物理的仿真模型和元启发式算法的现有研究会产生高计算成本,这使得实际实施具有挑战性。在本研究中,我们开发了自适应域 Nelder-Mead (ADNM) 方法,该方法优化了仿真模型的校准系数,以在短时间内最大限度地减少预测值和测量数据之间的误差。ADNM 使用 Sobol 序列和以前的最优解构建初始单纯形形,即使在操作条件发生变化的情况下也能稳定地最小化目标函数。与传统的 NM 算法相比,使用真实 HVAC 数据验证的所提出的方法将温度 RMSE 降低了 60-70%,并且在类似的计算时间内,温度误差比贝叶斯优化 (BO) 低 10-60%。尽管遗传算法 (GA) 实现了较低的误差,但其计算时间高出 3 到 6 倍以上,突出了所提出的方法的卓越效率和准确性。
更新日期:2025-01-18
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