Publications

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Leveraging hourly emission factors of electric grids to evaluate the operational performance of Canadian buildings

Published in Journal of Physics: Conference Series, 2025

Download the paper here Building electrification is seen as an essential means towards decarbonization, yet greenhouse gas emissions (GHG) due to building electricity use vary significantly depending on grid energy generation mixes. While total energy usage is still a primary concern, the timing of energy use becomes increasingly critical in terms of flexibility, energy costs and GHG emissions. This paper investigates how hourly average and marginal emission factors of different electric grids could impact the assessment of building GHG emissions. The proposed case study targets medium-office buildings located in two Canadian provinces (Québec, Ontario), equipped with a dual energy heating system and leveraging fuel switching during electric grid peak events to increase flexibility. Results show that calculations using average and marginal factors could lead to 44-106% differences, while in Ontario, fuel switching leads to an increase in GHG emissions using average factors but a decrease using marginal factors. These results laid the foundations for a broader discussion on appropriate metrics to assess building performance to properly drive building decarbonization strategies or policy. Recommended citation: Etienne Saloux and Kun Zhang. (2025) Journal of Physics: Conference Series.3140.052025. Read more

Development and validation of a generic evaporator model of chillers

Published in Proceedings of Building Simulation 2025: 18th Conference of IBPSA, 2025

Download the paper here Chillers consume approximately half of a building’s total energy, and variables within a chiller evaporator are crucial for system operation, control, and design of the secondary chilled water loop. Although detailed physical models like computational fluid dynamics are used to study evaporators, their complexity and high computational cost make them impractical for Heating, Ventilation, and Air Conditioning (HVAC) systems with limited data in a building automation system.This paper presents a grey-box model for chiller evaporators under steady-state conditions, integrating both physical and data-driven approaches. The model development starts with an analysis of the evaporator energy balance and heat transfer on both the water and refrigerant sides. It is then simplified into a practical equation with a simple format, requiring a minimal number of model input variables that are usually available in building automation system. The proposed model targets to estimate the chilled water temperature difference across the chiller evaporator. The case study of a real institutional building with a dataset at 15-minute intervals is used to validate the model performance. Results indicate it achieved a high accuracy with a coefficient of variance of root mean square error of 3.9%. The proposed model can be used to study HVAC operation optimization, fault detection and diagnosis, ultimately contributing to improved energy efficiency and system reliability. Recommended citation: Dou, H., Zhang, K. (2025). Development and validation of a generic evaporator model of chillers. In: Proceedings of Building Simulation 2025: 18th Conference of IBPSA, Aug 24-27, 2025, Brisbane, Australia. Read more

Transfer learning for cross-building forecasting of building energy and indoor air temperature in model predictive control applications

Published in Journal of Building Engineering, 2025

Download the paper here When applying Model Predictive Control (MPC) for Heating, Ventilation and Air Conditioning (HVAC) systems in buildings, accurate forecasting of short-term energy demand and indoor air condition profiles is essential. However, new or retrofitted buildings lack sufficient operation data to develop precise data-driven models. This study investigates transfer learning techniques to enhance the forecasting performance of black-box models under limited data conditions. Specifically, we leverage synthetic data from an open-source EnergyPlus building model to pre-train three neural network models, which are then transferred to a real building and fine-tuned with limited measurements. The results indicate that incorporating synthetic data into the pre-training phase significantly enhances the forecasting accuracy for building and HVAC energy, as well as indoor air temperature profiles, over a 12-h horizon with 15-min intervals. The study underscores the potential of combining transfer learning with synthetic data to address data limitations, extending the applicability of learning-based MPC in real-world buildings. Recommended citation: Dou, H., Zhang, K., 2025. Transfer learning for cross-building forecasting of building energy and indoor air temperature in model predictive control applications. Journal of Building Engineering 111, 113341. https://doi.org/10.1016/j.jobe.2025.113341 Read more

From theory to practice: A critical review of model predictive control field implementations in the built environment

Published in Applied Energy, 2025

Download the paper here While the potential of model-based predictive control (MPC) to improve building operation is widely acknowledged, its implementation has not yet become a mainstream practice in the building operation industry. This review paper explores the scientific literature documenting MPC field implementations in actual buildings. The goal is twofold: (a) to identify critical features in the deployment of MPC strategies, including the targeted building types and applications, systems controlled, expected benefits, software used, as well as common issues encountered (and successful measures to overcome these issues); (b) to evaluate the benefits of MPC based on the reported information from real-life implementations. Aspects analyzed include drivers and energy contexts, control-oriented modelling approaches, optimization routines, and performance evaluation methods. Results show that most practical studies focussed on buildings with a floor area under 10,000 m2, often even less than 1000 m2. MPC applications were varied, ranging from setpoint tracking and building conditioning to the optimization of the operation of thermal energy storage and photovoltaic panels and/or battery systems. MPC consistently yields significant benefits, with average savings of 30 % for thermal energy, 25 % for electricity use, 25 % for energy costs, 26 % for peak power and 17 % for GHG emissions, obtained under an average field-testing duration of 41 days. Recommended citation: Saloux, E., Candanedo, J.A., Vallianos, C., Morovat, N., Zhang, K., 2025. From theory to practice: A critical review of model predictive control field implementations in the built environment. Applied Energy 393, 126091. https://doi.org/10.1016/j.apenergy.2025.126091 Read more