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Digital twins have emerged as a promising concept for improving building energy efficiency, but their implementation faces challenges in interoperability and adaptability. This paper presents a large-scale field demonstration of an interoperable energy modeling framework for building digital twins, using ontology-based semantic models as data sources for automated model generation and calibration of data-driven component models. The study focuses on a single floor of a hospital building, comprising 12 conditioned zones and data from 45 measuring devices. Across the 45 sensors, the model achieved average mean absolute errors of 0.40∘C for temperature, 32 ppm for CO2 concentration, 0.06 for valve position, and 0.04 for damper position predictions. These results demonstrate the framework's ability to generate and calibrate accurate and flexible building energy models with reduced effort. The paper also showcases the framework's practical application in exploring system modifications to improve indoor comfort, highlighting its potential for scenario analysis and decision support. The proposed approach significantly streamlines the process of creating and maintaining accurate, up-to-date energy models, offering a robust foundation for digital twin applications in the built environment.

Digital twins have emerged as a promising concept for improving building energy efficiency, but their implementation faces challenges in interoperability and adaptability. This paper presents a large-scale field demonstration of an interoperable energy modeling framework for building digital twins, using ontology-based semantic models as data sources for automated model generation and calibration of data-driven component models. The study focuses on a single floor of a hospital building, comprising 12 conditioned zones and data from 45 measuring devices. Across the 45 sensors, the model achieved average mean absolute errors of 0.40∘C for temperature, 32 ppm for CO2 concentration, 0.06 for valve position, and 0.04 for damper position predictions. These results demonstrate the framework's ability to generate and calibrate accurate and flexible building energy models with reduced effort. The paper also showcases the framework's practical application in exploring system modifications to improve indoor comfort, highlighting its potential for scenario analysis and decision support. The proposed approach significantly streamlines the process of creating and maintaining accurate, up-to-date energy models, offering a robust foundation for digital twin applications in the built environment.

This paper aims to develop a green building meta-model for a representative passively designed high-rise residential building in Hong Kong. Modelling experiments are conducted with EnergyPlus to explore a Monte Carlo regression approach, which intends to interpret the relationship between input parameters and output indices of a generic building model and provide reliable building performance predictions. Input parameters are selected from different passive design strategies including the building layout, envelop thermophysics, building geometry and infiltration & air-tightness, while output indices are corresponding indoor environmental indices of the daylight, natural ventilation and thermal comfort to fulfil current green building requirements. The variation of sampling size, application of response transformation and bootstrap method, as well as different statistical regression models are tested and validated through separate modelling datasets. A sampling size of 100 per regression coefficient is determined from the variation of sensitivity coefficients, coefficients of determination and prediction uncertainties. The rank transformation of responses can calibrate sensitivity coefficients of a non-linear model, by considering their variation obtained from sufficient bootstrapping replications. Furthermore, the acquired meta-model with MARS (Multivariate Adaptive Regression Splines) is proved to have better model fitting and predicting performances. This research can accurately identify important architectural design factors and make robust building performance predictions associated with the green building assessment. Sensitivity analysis results and obtained meta-models can improve the efficiency of future optimization studies by pruning the problem space and shorten the computation time.

This paper aims to develop a green building meta-model for a representative passively designed high-rise residential building in Hong Kong. Modelling experiments are conducted with EnergyPlus to explore a Monte Carlo regression approach, which intends to interpret the relationship between input parameters and output indices of a generic building model and provide reliable building performance predictions. Input parameters are selected from different passive design strategies including the building layout, envelop thermophysics, building geometry and infiltration & air-tightness, while output indices are corresponding indoor environmental indices of the daylight, natural ventilation and thermal comfort to fulfil current green building requirements. The variation of sampling size, application of response transformation and bootstrap method, as well as different statistical regression models are tested and validated through separate modelling datasets. A sampling size of 100 per regression coefficient is determined from the variation of sensitivity coefficients, coefficients of determination and prediction uncertainties. The rank transformation of responses can calibrate sensitivity coefficients of a non-linear model, by considering their variation obtained from sufficient bootstrapping replications. Furthermore, the acquired meta-model with MARS (Multivariate Adaptive Regression Splines) is proved to have better model fitting and predicting performances. This research can accurately identify important architectural design factors and make robust building performance predictions associated with the green building assessment. Sensitivity analysis results and obtained meta-models can improve the efficiency of future optimization studies by pruning the problem space and shorten the computation time.

Building-Integrated Concentrated Photovoltaics (BICPV) is based on Photovoltaic (PV) technology which experience a loss in their electrical efficiency with an increase in temperature that may also lead to their permanent degradation over time. With a global PV installed capacity of 303 GW, a nominal 10 °C decrease in their average temperature could theoretically lead to 15 GW increase in electricity production worldwide. Currently, there is a gap in the research knowledge concerning the effectiveness of the available passive thermal regulation techniques for BICPV, both individually and working in tandem. This paper presents a novel combined passive cooling solution for BICPV incorporating micro-fins, Phase Change Material (PCM) and Nanomaterial Enhanced PCM (n-PCM). This work was undertaken with the aim to assess the unreported to date benefits of introducing these solutions into BICPV systems and to quantify their individual as well as combined effectiveness. The thermal performance of an un-finned metallic plate was first compared to a micro-finned plate under naturally convective conditions and then compared with applied PCM and n-PCM. A designed and fabricated, scaled-down thermal system was attached to the electrical heaters to mimic the temperature profile of the BICPV. The results showed that the average temperature in the centre of the system was reduced by 10.7 °C using micro-fins with PCM and 12.5 °C using micro-fins with n-PCM as compared to using the micro-fins only. Similarly, the effect of using PCM and n-PCM with the un-finned surface demonstrated a temperature reduction of 9.6 °C and 11.2 °C respectively as compared to the case of natural convection. Further, the innovative 3-D printed PCM containment, with no joined or screwed parts, showed significant improvements in leakage control. The important thermophysical properties of the PCM and the n-PCM were analysed and compared using a Differential Scanning Calorimeter. This research can contribute to bridging the existing gaps in research and development of thermal regulation of BICPV and it is envisaged that the realised incremental improvement can be a potential solution to (a) their performance improvement and (b) longer life, thereby contributing to the environmental benefits.

Building-Integrated Concentrated Photovoltaics (BICPV) is based on Photovoltaic (PV) technology which experience a loss in their electrical efficiency with an increase in temperature that may also lead to their permanent degradation over time. With a global PV installed capacity of 303 GW, a nominal 10 °C decrease in their average temperature could theoretically lead to 15 GW increase in electricity production worldwide. Currently, there is a gap in the research knowledge concerning the effectiveness of the available passive thermal regulation techniques for BICPV, both individually and working in tandem. This paper presents a novel combined passive cooling solution for BICPV incorporating micro-fins, Phase Change Material (PCM) and Nanomaterial Enhanced PCM (n-PCM). This work was undertaken with the aim to assess the unreported to date benefits of introducing these solutions into BICPV systems and to quantify their individual as well as combined effectiveness. The thermal performance of an un-finned metallic plate was first compared to a micro-finned plate under naturally convective conditions and then compared with applied PCM and n-PCM. A designed and fabricated, scaled-down thermal system was attached to the electrical heaters to mimic the temperature profile of the BICPV. The results showed that the average temperature in the centre of the system was reduced by 10.7 °C using micro-fins with PCM and 12.5 °C using micro-fins with n-PCM as compared to using the micro-fins only. Similarly, the effect of using PCM and n-PCM with the un-finned surface demonstrated a temperature reduction of 9.6 °C and 11.2 °C respectively as compared to the case of natural convection. Further, the innovative 3-D printed PCM containment, with no joined or screwed parts, showed significant improvements in leakage control. The important thermophysical properties of the PCM and the n-PCM were analysed and compared using a Differential Scanning Calorimeter. This research can contribute to bridging the existing gaps in research and development of thermal regulation of BICPV and it is envisaged that the realised incremental improvement can be a potential solution to (a) their performance improvement and (b) longer life, thereby contributing to the environmental benefits.

To promote the sustainable development of supply chains, numerous manufacturers are embracing corporate environmental responsibility to mitigate the ecological consequences of production activities. The environmental benefit concern (EBC) of enterprises not only affects their operations strategies but also impacts other enterprises within the supply chains. Therefore, it is essential to study the influences of EBC behaviors on supply chain performances under various competitive scenarios. This paper examines a supply chain comprising two manufacturers and one retailer under two distinct competitive settings: Competition between non-green and green products (OE), and competition between green products from small and big brands (SB). The study analyzes the effects of unilateral, bilateral or multilateral EBC behaviors on both the economic and environmental benefits of the supply chain. Key findings emerge from this study. Firstly, prioritizing environmental benefits throughout the supply chain has the potential to enhance environmental performance. Secondly, unilateral EBC behavior can lead to a vertical altruistic effect, where the focus on environmental benefit by a manufacturer (retailer) increases the retailer's (manufacturer) profit but reduces its own profit. Additionally, unilateral EBC behavior among manufacturers creates a competitive squeezing effect on each other, potentially diminishing competitor profits. Notably, the retailer's focus on environmental benefit can also reduce the profit of the non-green manufacturer. Thirdly, when the levels of EBC are relatively low, bilateral or multilateral through EBC leads to a positive effect on economic performance, resulting in increased profits for both the green manufacturers and retailer. In conclusion, bilateral EBC has the potential to enhance both economic and environmental benefits in the OE scenario.