• Energy Research

Building integrated photovoltaics (BIPV) presents a great opportunity for decreasing building energy demand and related CO2 emissions, specially in the refurbishment of old isolated high rise highly glazed office buildings. This article presents a simulation study of the impact of BIPV on a Spanish office building of the 60’s, aiming to serve as a reference for this type of buildings in the Mediterranean region. Upgrade of the envelope with BIPV is evaluated. The solutions for the glazing system include conventional solar control window and a transparent photovoltaic prototype window. On the opaque part, commercial BIPV is considered. A comprehensive model integrating the BIPV impact in walls in windows for the thermal, electrical and daylighting is presented. Dynamic simulations carried out with TRNSYS software allow to evaluate the impact on daylighting, energy demand and economics. The results show that transparent BIPV reduced the energy demand by 6.9% and the total energy balance by 21%. The opaque BIPV further improved these results achieving a 38.3% reduction in the energy balance. Moreover, transparent BIPV also reduces the hours with excessive daylighting, although at the cost of reduced daylighting autonomy. The economic analysis highlights the importance of electricity pricing schedules in the promotion of BIPV, comparing current tariff structure in Spain and the duck chart from California Independent Operator. Results show the capabilities of this technology and provides guidelines for investment cost and efficiency targets.

Building integrated photovoltaics (BIPV) presents a great opportunity for decreasing building energy demand and related CO2 emissions, specially in the refurbishment of old isolated high rise highly glazed office buildings. This article presents a simulation study of the impact of BIPV on a Spanish office building of the 60’s, aiming to serve as a reference for this type of buildings in the Mediterranean region. Upgrade of the envelope with BIPV is evaluated. The solutions for the glazing system include conventional solar control window and a transparent photovoltaic prototype window. On the opaque part, commercial BIPV is considered. A comprehensive model integrating the BIPV impact in walls in windows for the thermal, electrical and daylighting is presented. Dynamic simulations carried out with TRNSYS software allow to evaluate the impact on daylighting, energy demand and economics. The results show that transparent BIPV reduced the energy demand by 6.9% and the total energy balance by 21%. The opaque BIPV further improved these results achieving a 38.3% reduction in the energy balance. Moreover, transparent BIPV also reduces the hours with excessive daylighting, although at the cost of reduced daylighting autonomy. The economic analysis highlights the importance of electricity pricing schedules in the promotion of BIPV, comparing current tariff structure in Spain and the duck chart from California Independent Operator. Results show the capabilities of this technology and provides guidelines for investment cost and efficiency targets.

The development of Urban-Scale Energy Modelling (USEM) at the district or city level is currently the goal of many research groups due to the increased interest in evaluating the impact of energy efficiency measures in city environments. Because USEM comprises a great variety of analysis areas, the simulation programs that are able to model urban-scale energy systems actually consist of an assemblage of different particular sub-models. In order to simulate each of the sub-models in USEM, one can choose to use either existing specific simulation engines or tailor-made models. Engines or tools for simulation of urban-scale energy systems have already been overviewed in previous existing literature, however the distinction and classification of tools according to their functionalities within each analysis area in USEM has not been clearly presented. Therefore, the present work aims at reviewing the existing tools while classifying them according to their capabilities. The ultimate goal of this classification is to expose the available resources for implementing new co-simulation approaches in USEM, which may reduce the modelling effort and increase reliability as a result of using established and validated simulation engines.

The development of Urban-Scale Energy Modelling (USEM) at the district or city level is currently the goal of many research groups due to the increased interest in evaluating the impact of energy efficiency measures in city environments. Because USEM comprises a great variety of analysis areas, the simulation programs that are able to model urban-scale energy systems actually consist of an assemblage of different particular sub-models. In order to simulate each of the sub-models in USEM, one can choose to use either existing specific simulation engines or tailor-made models. Engines or tools for simulation of urban-scale energy systems have already been overviewed in previous existing literature, however the distinction and classification of tools according to their functionalities within each analysis area in USEM has not been clearly presented. Therefore, the present work aims at reviewing the existing tools while classifying them according to their capabilities. The ultimate goal of this classification is to expose the available resources for implementing new co-simulation approaches in USEM, which may reduce the modelling effort and increase reliability as a result of using established and validated simulation engines.

The use of dynamic resistance–capacitance (RC) grey-box models to estimate building thermal energy demands is a well-known strategy. Nonetheless, there are no clear methodologies to generate model variations without having to re-identify the parameters that are simple, flexible, accurate and low computation demanding. This research introduces a simple methodology that combines parameter identification from white box generated data and simplified theoretical building thermal equations to create functional and reliable RC model variations from an original grey-box model. It demonstrates that the original RC grey-box parameters can be altered based on theoretical value variation factors to represent different building renovation scenarios accurately. The study provides an overview of the grey-box modelling process (R2C2 model), highlighting the flexibility of RC parameters and their impact on building thermal behaviour. The initially generated grey-box model parameters are adapted to various scenarios, involving common building envelope alterations that are seen in renovations. The thermal energy demands from the adjusted grey-box model are compared with those from a detailed white-box model, confirming the reliability of the proposed methodology. The results prove the adaptability and robustness of the R2C2 parameters, enabling a new strategy that reduces modelling complexity and time while maintaining high accuracy. This advancement offers a fresh perspective for creating large-scale building retrofitting models, urban simulation tools, microclimate studies, renewable energy integration, predictive control models, and other applications requiring simplified, rapid and reliable simulations.

The use of dynamic resistance–capacitance (RC) grey-box models to estimate building thermal energy demands is a well-known strategy. Nonetheless, there are no clear methodologies to generate model variations without having to re-identify the parameters that are simple, flexible, accurate and low computation demanding. This research introduces a simple methodology that combines parameter identification from white box generated data and simplified theoretical building thermal equations to create functional and reliable RC model variations from an original grey-box model. It demonstrates that the original RC grey-box parameters can be altered based on theoretical value variation factors to represent different building renovation scenarios accurately. The study provides an overview of the grey-box modelling process (R2C2 model), highlighting the flexibility of RC parameters and their impact on building thermal behaviour. The initially generated grey-box model parameters are adapted to various scenarios, involving common building envelope alterations that are seen in renovations. The thermal energy demands from the adjusted grey-box model are compared with those from a detailed white-box model, confirming the reliability of the proposed methodology. The results prove the adaptability and robustness of the R2C2 parameters, enabling a new strategy that reduces modelling complexity and time while maintaining high accuracy. This advancement offers a fresh perspective for creating large-scale building retrofitting models, urban simulation tools, microclimate studies, renewable energy integration, predictive control models, and other applications requiring simplified, rapid and reliable simulations.