Radiant Conditioning Retrofitting for Residential Buildings

In order to achieve Australia’s greenhouse gas emissions reduction targets, a majority of the existing residential building stock in Australia will require retrofitting in favour of energy-efficient solutions. This paper considers retrofitting for conditioning to be one of the most straightforward and offers the greatest potential to deliver significant comfort and energy-saving results. Radiant conditioning systems are not new, yet some game-changing innovations have taken place over the last decade that may require an entire paradigm shift in the manner we condition our buildings. The reiteration of the principle ‘thermally active systems’ suggests that our buildings need to accommodate these systems into the fabric of building components. However, extremely few products and/or innovative solutions for doing such seem to be provided by the industry. We seem incompetent with solutions that are not costing the Earth, insulating, lightweight, and offering an instant response time to conditioning. We still have the concept embedded in our minds that radiative systems consist of heavy ‘combat’ construction with time lags of a day or two and that they are very costly to implement, especially if we are to retrofit a project. The purpose of this paper is to rectify and change our understanding of radiant systems, namely through a review of the existing technology and its recent advancements. It intends to introduce the fact that radiant systems can become highly reactive, responsive, and thermally dynamic conditioning systems. Lightweight radiant systems can be 40% more energy-efficient than common air conditioners and can respond in less than 15 min rather than in the hours required of heavy radiant systems. Thus, an insulated, lightweight radiant system is ideal for retrofitting residential buildings. Furthermore, this paper supports and introduces various systems suited to retrofitting a residential building with hydronic radiant systems.

Application of Artificial Neural Networks in the Urban Building Energy Modelling of Polish Residential Building Stock

The development of energy-efficient buildings and sustainable energy supply systems is an obligatory undertaking towards a more sustainable future. To protect the natural environment, the modernization of urban infrastructure is indisputably important, possible to achieve considering numerous buildings as a group, i.e., Building Energy Cluster (BEC). The urban planning process evaluates multiple complex criteria to select the most profitable scenario in terms of energy consumption, environmental protection, or financial profitability. Thus, Urban Building Energy Modelling (UBEM) is presently a popular approach applied for studies towards the development of sustainable cities. Today’s UBEM tools use various calculation methods and approaches, as well as include different assumptions and limitations. While there are several popular and valuable software for UBEM, there is still no such tool for analyses of the Polish residential stock. In this work an overview on the home-developed tool called TEAC, focusing on its’ mathematical model and use of Artificial Neural Networks (ANN). An exemplary application of the TEAC software is also presented.

Residential building stock modelling for mainland China targeted for seismic risk assessment

To enhance the estimation accuracy of economic loss and casualty in seismic risk assessment, a high-resolution building exposure model is necessary. Previous studies in developing global and regional building exposure models usually use coarse administrative-level (e.g. country or sub-country level) census data as model inputs, which cannot fully reflect the spatial heterogeneity of buildings in large countries like China. To develop a high-resolution residential building stock model for mainland China, this paper uses finer urbanity-level population and building-related statistics extracted from the records in the tabulation of the 2010 population census of the People's Republic of China (hereafter abbreviated as the “2010 census”). In the 2010 census records, for each province, the building-related statistics are categorized into three urbanity levels (urban, township, and rural). To disaggregate these statistics into high-resolution grid level, we need to determine the urbanity attributes of grids within each province. For this purpose, the geo-coded population density profile (with 1 km × 1 km resolution) developed in the 2015 Global Human Settlement Layer (GSHL) project is selected. Then for each province, the grids are assigned with urban, township, or rural attributes according to the population density in the 2015 GHSL profile. Next, the urbanity-level building-related statistics can be disaggregated into grids, and the 2015 GHSL population in each grid is used as the disaggregation weight. Based on the four structure types (steel and reinforced concrete, mixed, brick and wood, other) and five storey classes (1, 2–3, 4–6, 7–9, ≥10) of residential buildings classified in the 2010 census records, we reclassify the residential buildings into 17 building subtypes attached with both structure type and storey class and estimate their unit construction prices. Finally, we develop a geo-coded 1 km × 1 km resolution residential building exposure model for 31 provinces of mainland China. In each 1 km × 1 km grid, the floor areas of the 17 residential building subtypes and their replacement values are estimated. The model performance is evaluated to be satisfactory, and its practicability in seismic risk assessment is also confirmed. Limitations of the proposed model and directions for future improvement are discussed. The whole modelling process presented in this paper is fully reproducible, and all the modelled results are publicly accessible.

Estimating the Smart Readiness Indicator in the Italian Residential Building Stock in Different Scenarios

The Energy Performance of Buildings Directive 2018/844/EU introduced the smart readiness indicator (SRI) to provide a framework to evaluate and promote building smartness in Europe. In order to establish a methodological framework for the SRI calculation, two technical studies were launched, at the end of which a consolidated methodology to calculate the SRI of a building basing on a flexible and modular multicriteria assessment has been proposed. In this paper the authors applied the above-mentioned methodology to estimate the SRI of the Italian residential building stock in different scenarios. To this end, eight “smart building typologies”, representative of the Italian residential building stock, have been identified. For each smart building typology, the SRI was calculated in three scenarios: (a) base scenario (building stock as it is); (b) an “energy scenario” (simple energy retrofit) and (c) a “smart energy scenario” (energy retrofit from a smart perspective). It was therefore possible to estimate a national average SRI value of 5.0%, 15.7%, and 27.5% in the three above defined scenarios, respectively.

A System Dynamics Model for Urban Residential Building Stock towards Sustainability: The Case of Jinan, China

Resource and environmental issues related to urban building systems have recently become a hot research topic in the field of urban environmental management research. Taking Jinan city as an example, this paper establishes a system dynamic model for an urban residential building stock system. The simulated results show that the urban residential building stock will be 1.99 × 108 m2 in 2050; and the annual total demolition buildings will be at 3.36 × 106 m2 in 2082. Policy measures were developed based on four important action fields such as per capita floor area (PCFA), the building structure proportion of new construction, lifetime of the residential building, and the recycling of the C&D waste. Among these approaches, the set of policy measures focusing on the recycling of C&D waste appears to be more effective in reducing environmental and resource impacts than the other three fields. It is also found that the recycling of brick and concrete waste plays a considerable role in reducing environment and resource impacts due to the development of urban residential building stock with the lapse of time.