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Building energy design is currently going through a period of major changes.One key factor of this is the adoption of net-zero energy as a long term goal for new buildings in most developed countries.To achieve this goal a lot of research is needed to accumulate knowledge and to utilize it in practical applications.

This paper presents thermal resistance-capacitance (RC) network models developed from the parametric simulations of building archetypes for early design stage energy demand assessment. Building energy performance is typically measured using physics-based simulation tools that require expert knowledge, high computational cost and detailed building information that are unavailable at the early design stage. These barriers make the white-box models inefficient for multi-scenario analyses of near-optimal building control and operation strategies. The thermal RC networks are typical grey-box models that use simplified physical descriptions to simulate a building's thermal behaviour while retaining a state-space formulation that can be efficiently solved for different parametric simulations and optimization. Previous publications have explored the comparative performance of physics-based and data-driven models for optimal building operations, but there is a marked scarcity of studies on the parametric translation of design-oriented white-box models to thermal RC networks at the conceptual stage of building energy prediction. This paper presents a link between white- and grey-box modelling approaches for early design stage energy assessment of residential buildings by (i) developing multiple white-box building models through parametric modifications of window-to-wall ratio (WWR), total floor area and envelope properties (ii) reducing complexities of multi-scenario simulations into grey-box models and (iii) investigating the sensitivity of the model parameters to the simulated scenarios. For the parametric modifications of floor areas between 150 to 300 [m.sup.2] (1615 to 3229[ft.sup.2]), WWR of 20% to 60%, and envelope gypsum boards of 1 to 4 layers, the results yielded (i) Effective air capacitance ranges from 1x[10.sup.7] to 1.4x[10.sup.7]J/K (9.5x[10.sup.3] to 1.3x[10.sup.4]BTU/[degrees]F) and (ii) Envelope capacitance values between 2.2x[10.sup.7]J/K to 3x[10.sup.7]J/K (2 .1x[10.sup.4] to 2.8x[10.sup.4]BTU/[degrees]F) These findings extend the domain knowledge for quantifying the initial parameter values of thermal capacitances in the residential building stock. This study also presents a clustering approach to designers and energy system operators for multi-criteria design and control decisions with reduced computational overhead in aggregate energy prediction at the early design stage.

The transition from traditional to smart grid operations necessitates studies that assess buildings potential for providing energy flexibility to the electrical grid. This paper investigates the effects of optimizing thermal and electrical loads in a building cluster supported by a microgrid equipped with photovoltaic panels and battery storage systems. It examines how leveraging building thermal mass can flatten the load profile and support the operation of the connected microgrid. This simulation study employs a data-driven methodology based on Resistance-Capacitance thermal networks and machine learning. Using these techniques, the aggregated thermal and electrical loads of the buildings are evaluated to develop an energy management strategy based on model predictive control. The model predictive control strategy aims to optimize grid interaction by enhancing self-consumption and energy flexibility, quantified in terms of load leveling and flattening. The study includes simulations with variations of key parameters to assess performance under different scenarios. The case study focuses on a virtual community located in Varennes, Québec, Canada, utilizing data from institutional and residential buildings, including the Varennes Library-the first Net Zero institutional building in Canada-and its surrounding buildings. These buildings are equipped with building automation systems and smart thermostats/meters, providing access to real-time operational data. Results for typical days, identified through the k-means clustering technique, demonstrate that the ratio between average daily energy consumption and peak energy consumption of the virtual community can increase by over 80% with the utilization of communal battery storage in the microgrid. Additionally, integrating thermal load management through strategic temperature setpoint variations within buildings can half of the increase of the load factor, potentially reducing the required battery size.

Energy system planning is increasingly challenged by uncertainty in electricity markets, technology evolution, and operational variability. Computational models used for building- and district- energy system planning must balance realistic uncertainty representation with computational efficiency. This study advances stochastic energy planning by proposing a structured approach for modelling the electricity price uncertainty, examining four stochastic processes: Geometric Brownian Motion, Arithmetic Brownian Motion, the Ornstein-Uhlenbeck process, and a time-dependent Ornstein-Uhlenbeck variant. The processes were calibrated using Maximum Likelihood Estimation on historical Cypriot tariff data. The evaluation results across four historical time periods showed that the time-dependent Ornstein-Uhlenbeck process provides the most accurate representation of price uncertainty, achieving an average mean absolute percentage error of 14.4% and an average root mean square error of 0.043, outperforming the other processes. Representative price paths were then extracted using density-based clustering to integrate price forecasts smoothly into an established whole-energy building design and operation optimization model. Overall, the study offers a transparent and reproducible methodology for embedding representative price paths into building energy system optimization frameworks, supporting the optimal selection and operation of technologies at both building and district scales.

The use of heat recovery ventilators (HRVs) in cold climate housing is becoming increasingly common, to provide outside air to the occupied space while recovering heat from the exhausted air. However, frosting of the heat exchanger core impedes HRV operation in cold climate conditions. Strategies for defrosting the core include recirculating the exhaust airstream while stopping the supply of the outside air during the defrost cycle. Preheating the air using heating coils is also used, but its application is limited by the increased energy use of the preheater. This study aims to investigate the impact of a building integrated photovoltaic/thermal (BIPV/T) system on the HRV thermal performance by using it to preheat the outside air. In this experimental study, measurements of a heat recovery ventilator preheated by the BIPV/T system are carried out in a test cell of the Future Buildings Laboratory (FBL) at Concordia University in Montreal, Canada. The results are compared to a HRV without BIPV/T preheating. The relative humidity, temperature and airflow rate of the fresh air and exhaust airstreams are monitored at the inlet and outlet of the HRV. The experiments are conducted during two weeks from January 26 th , 2023 to February 08 th , 2023. The change of the sensible heat recovery effectiveness ∈ sens of the HRV, outside air outlet temperature T 2 from the HRV and outside air flow rate, ṁ 1 and exhaust air flow rate ṁ 4 are determined. The primary experimental results indicate that preheating the outside air by the BIPV/T system before entering the HRV can help to increase the outside air outlet temperature T 2 by up to 2.7°C at an outdoor temperature of -25 °C, and the sensible heat recovery effectiveness ∈ sens , outside air flow rate, ṁ 1 (23L/s) and exhaust air flow rate ṁ 4 (43L/s) of the HRV are kept constant at the same level with preheating by BIPV/T on the HRV at -5 °C to -25 °C of outdoor temperature.

Operational digital twins are transitioning from static \"as-built\" representations to dynamic, real-time decision-support systems that require robust predictive capabilities to close the loop between monitoring and control. Existing black-box approaches often lack physical interpretability and generalizability, whereas traditional white-box physics-based models face deployment challenges, including complex calibration, high-dimensional parameter spaces, and computational intensity for real-time use. To address these limitations, this study presents a proof-of-concept grey-box modeling framework as a foundational step toward ontology-driven digital twins for building performance optimization. Custom low-order thermal network models are developed and calibrated against operational data from a living-lab office at the University of Cyprus. The models are formulated in a stochastic state-space representation, and parameters are estimated via inverse modeling techniques using Maximum Likelihood Estimation, with an additional Bayesian inference option (Markov Chain Monte Carlo) to quantify parameter and predictive uncertainty. Results show that the models reproduce indoor air temperature with high fidelity (RMSE [approximately equal to] 0.20 [degrees]C), while probabilistic forecasts provide meaningful confidence bounds to support risk-informed decision-making. This work demonstrates the integration of physically interpretable grey-box models with semantic data interoperability in an initial prototype, establishing a pathway toward real-time, ontology-enabled digital twins. The study highlights both the feasibility and practical value of the approach, while outlining the necessary steps to achieve continuous real-time calibration and performance optimization in future implementations.

Electrification is an efficient way to decarbonize by replacing fossil fuels with low-emission power. In addition, energy efficiency measures can reduce consumption, making it easier to shift to a zero-carbon society. In Québec, upgrades to aging buildings that employ electric resistance heating offer a unique opportunity to free up large amounts of hydroelectricity that can serve to decarbonize heating in other buildings. However, another source of energy would be needed to electrify mobility because efficiency measures free up small amounts of electricity in summer compared to winter. This study reveals how building efficiency measures combined with solar electricity generation provide an energy profile that matches the requirements for decarbonizing both mobility and heating. The TRNSYS software was used to simulate the annual energy performance of an existing house and retrofitted/rebuilt low-energy houses equipped with a photovoltaic (PV) roof in Montreal, Québec, Canada (45.5° N). The electricity that is made available by upgrading the houses is mainly considered for powering battery and fuel cell electric vehicles (BEVs and FCEVs) and electrifying heating in greenhouses. The results indicate that retrofitting 16% or rebuilding 12% of single-detached homes in Québec can provide enough electricity to decarbonize heating energy use in existing greenhouses and to operate the new greenhouses required for growing all fresh vegetables locally. If all the single-detached houses that employ electric resistance heating are upgraded, 33.4 and 21.8 TWh year−1 of electricity would be available for decarbonization, equivalent to a 19% and 12% increase of the province’s electricity supply for the retrofitted or rebuilt houses, respectively. This is enough energy to convert 83–100% of personal vehicles to BEVs or 35–56% to FCEVs. Decarbonization using the electricity that is made available by upgrading to low-energy solar houses could reduce the province’s greenhouse gas (GHG) emissions by approximately 32% (26.5 MtCO2eq). The time required for the initial embodied GHG emissions to surpass the emissions avoided by electrification ranges from 3.4 to 11.2 years. Building energy efficiency retrofits/rebuilds combined with photovoltaics is a promising approach for Québec to maximize the decarbonization potential of its existing energy resources while providing local energy and food security.

The electrification of end uses and the rapid deployment of distributed energy resources are transforming buildings into active participants in grid-interactive energy systems. Unlocking this potential requires digital infrastructures capable of harmonizing fragmented telemetry, ensuring interoperability, and embedding flexibility attributes within operational control. This paper presents the development and validation of a semantic digital twin for the University of Cyprus (UCY) nanogrid, a living lab integrating photovoltaics, battery storage, electric vehicle charging, centralized cooling, smart calorimeters, and weather monitoring under supervisory SCADA management. The proposed methodology employs a layered semantic pipeline that combines Brick Schema, ASHRAE Standard 223P, the Energy Flexibility Ontology (EFOnt), and the Resistance -Capacitance Ontology (RCOnt). Brick Schema and ASHRAE 223P provide structural and device-level metadata, EFOnt formalizes controllability and service participation, and RCOnt encodes simplified 1R1C thermal models linked to building zones. Together, these annotations transform heterogeneous data streams into a coherent, machine-interpretable knowledge graph. Results demonstrate that subsystem diversity can be systematically unified into a queryable and extensible representation that preserves structural detail, characterizes flexibility potential, and incorporates thermal dynamics for predictive applications. The case study confirms the feasibility of implementing ontology-driven digital twinning in a campus nanogrid, highlighting its value for asset discovery, consistency validation, and integration with supervisory control strategies. The study establishes a reproducible foundation for scaling semantic twins to community infrastructures. Ongoing work focuses on calibrating the embedded thermal models with operational data to enable their use in supervisory predictive control, advancing semantic twins as active enablers of flexibility-oriented energy management.

This paper examines the early performance of the Varennes Library, a building designed for net-zero annual energy balance in Varennes, near Montreal, Canada. It produces electricity from a 110.5 kWp building-integrated photovoltaic (BIPV) system where heatis also recovered from a section of the array and used to preheat the outdoor air intake. The building's many architectural and mechanical features were integrally designed to achieve the net zero energy target over a five-year averaging period with several key decisions made at the early design stage. These include the shape, area, and orientation of the roof that maximizes electricity production from the BIPV (part BIPV/T [building-integrated photovoltaic/thermal with heat recovery]) system and a design layout that promotes daylight penetration and natural ventilation/free cooling during the cooling season. In the first year after inauguration, an operational energy use intensity (EUI) of 24.8 kBtu/[ft.sup.2]y (78.1 kWh/[m.sup.2]y) was achieved and has since been reduced to 22.20 kBtu/[ft.sup.2]y (70.0 kWh/[m.sup.2]y). Considering renewables production, the net-energy use intensity (EUI) is 4.60 kBtu/[ft.sup.2]y (14.5 kWh/[m.sup.2]y). This is a 95% EUI reduction over the national institutional average and can be further reduced with additional (ongoing) commissioning efforts. Suggested improvements in operation include ensuring the electricity production is optimized and any faults corrected, dimming electric lighting when daylight is sufficient, extending the hours of natural ventilation, and better utilization of the hydronic radiant slab for thermal storage using predictive controls. This paper discusses the process followed in the design of the library, its key features, its early performance, and some of the lessons learned.

Energy and life cycle cost analysis were employed to identify the most-cost effective ground envelope design for a greenhouse that employs supplemental lighting located in Ottawa, Ontario, Canada (45.4° N). The envelope design alternatives that were investigated consist of installing insulation vertically around the perimeter and horizontally beneath the footprint of a greenhouse with a concrete slab and unfinished soil floor. Detailed thermal interaction between the greenhouse and the ground surface is achieved by considering 3-dimensional conduction heat transfer within the TRNSYS 17.2 simulation software. The portion of total heat loss that occurred through the ground was approximately 4% and permutations in ground insulation design reduced heating energy consumption by up to 1%. For the two floor designs, the highest net savings was achieved when perimeter and floor zone horizontal insulation was installed whereas a financial loss occurred when it was also placed beneath the crop zone. However, in all cases, the improvement in economic performance was small (net savings below $4000 and reduction in life cycle under 0.2%). Combined energy and life cycle cost analysis is valuable for selecting optimal envelope designs that are capable of lowering energy consumption, improving economics and enhancing greenhouse durability.