Jaewan Joe

Recent building envelopes have shifted from static to advanced forms, including building-integrated photovoltaics (BIPV), chromogenic glazing, dynamic shading systems, phase change material (PCM) envelopes, envelope-integrated decentralized ventilation and heat pumps, and their hybrid combinations. However, typical building energy simulation (BES) tools were originally designed under static-envelope assumptions, leading to modeling limitations. This systematic review provides an analysis of physical models, control models, and modeling limitations across six categories of advanced envelope systems. Cross-system comparison reveals two recurring patterns: model simplification, where time-varying or non-linear physics is reduced to static or discrete formulations, and absence of dedicated modules, requiring ad-hoc implementation methods such as third-party tools or plugins. Quantitatively, neglecting PCM hysteresis shifts the predicted hours below the transition temperature by 10.6–29.4%, diffuse-slat optical models produce up to 40% daylight-transmittance error for specular shading slats, a 1 °C reduction in thermochromic transition temperature increases predicted savings by 1.5–3%, and the heat-recovery efficiency of decentralized ventilation drops by approximately 20 percentage points at a 30 Pa pressure difference, an effect that fixed-efficiency inputs cannot capture. Moreover, dedicated built-in modules exist only for TC glazing, PCM, and a recently added air-based BIPV/T object; the other categories rely on generic objects or user-side workarounds, and the coupling of four or more domains in hybrid systems is unsupported by any major BES tools. A short-, medium-, and long-term research roadmap is proposed, encompassing model accuracy improvements, dedicated module development, and FMI/FMU-based multi-domain co-simulation standardization, intended to support reliable performance prediction and design optimization of advanced envelope systems.

This study proposes a rule-based control strategy to improve the applicability of the model predictive control (MPC), with a specific focus on pre-cooling behavior. The grey-box building models were developed using the measurement data and MPC simulations were conducted under time-of-use (TOU) electricity pricing structures to generate cooling behavior data. Regression models to predict pre-cooling rates and corresponding control algorithms were developed using meteorological variables such as outdoor temperature and solar radiation. All models demonstrated strong predictive performance, with R² values exceeding 0.80. In simulation evaluation, the proposed rule-based MPC algorithm showed 99.36% and 97.10% of similarity for cost and electricity usage compared to the conventional MPC while achieving up to 10% of cost saving compared to the feedback control. In the experimental demonstration, the rule-based MPC reduced operational cost by 15.9-45.2% compared to the feedback control while maintaining the indoor thermal comfort inside the preferable bound.

This study proposes a penalty-based distributed MPC framework to simultaneously reduce electricity cost and mitigate peak load in buildings. The framework employs a resistance-capacitance (RC)-network-based grey-box thermal model developed from measured data. The framework also enables non-intrusive, automatic control through real-time data acquisition and application programming interface (API)-based integration with a commercial heating, ventilation, and air conditioning (HVAC) system. In the proposed framework, each zone independently solves an MPC problem to maintain indoor comfort, reduce operating costs, and satisfy operational constraints. At the same time, information exchange among zones enables coordinated load distribution. The proposed strategy was validated through both simulation and experimental implementation in a twin-chamber test bed. Simulation results show that the framework reduces electricity cost by 8.5% relative to the baseline feedback control and lowers the peak load by 49% relative to basic MPC. Experimental results confirm that the proposed method maintains indoor comfort while reducing the peak load by 43.7% and electricity cost by 11.0% relative to the baseline feedback control. These findings indicate that the proposed control framework is a practical and potentially scalable approach for building energy management applications.

This study proposes a rule-based control strategy to improve the applicability of the model predictive control (MPC), with a specific focus on pre-cooling behavior. The grey-box building models were developed using the measurement data and MPC simulations were conducted under time-of-use (TOU) electricity pricing structures to generate cooling behavior data. Regression models to predict pre-cooling rates and corresponding control algorithms were developed using meteorological variables such as outdoor temperature and solar radiation. All models demonstrated strong predictive performance, with R² values exceeding 0.80. In simulation evaluation, the proposed rule-based MPC algorithm showed 99.36% and 97.10% of similarity for cost and electricity usage compared to the conventional MPC while achieving up to 10% of cost saving compared to the feedback control. In the experimental demonstration, the rule-based MPC reduced operational cost by 13.6~41.3% compared to the feedback control while maintaining the indoor thermal comfort inside the preferable bound.

This study proposes a methodology for fairly assessing the building energy usage level of occupants using a public open dataset. A case study of day-care centers in South Korea was conducted to demonstrate the methodology. An open dataset of monthly building energy consumption in the day-care centers was obtained and grouped based on thermal performance (e.g., U-value). For each performance group, monthly electricity consumption (representing cooling demand), gas consumption (representing heating demand), and energy consumption were segmented using k-means clustering into heavy, medium, and light users. For each user cluster, representative monthly trajectories were ascertained by averaging the values. Using the input variables of the building performance and environmental factors, the machine learning-based evaluation models were developed to purely infer the impact of the occupants on energy consumption (monthly trajectories). All models exhibited reasonable performance (12% cv(RMSE) in the worst case); the linear regression model is recommended for its simplicity and applicability in policymaking and decision-making contexts. Finally, the efficacy of the developed model in evaluating energy usage levels is presented with an example.