Solar Thermal Regeneration of Borehole Heat Exchangers in Urban and Suburban Districts

To prevent undercooling of the ground in densely populated areas, regeneration of borehole heat exchangers (BHEs), for example by solar thermal heat, may become necessary. However, the usable roof area is often small compared to the building’s heat demand, especially in urban areas. It was investigated how much regeneration is possible in districts that are supplied entirely by heat pumps with BHEs. Example buildings were modelled based on the buildings of two districts in Zurich. Uncovered PVT collectors and glazed flat-plate collectors were used as regeneration sources. The possible regeneration was determined in a simulation process that included the effects of mutual influences between the BHEs of neighbouring buildings. As expected, glazed flat-plate collectors allow for more regeneration than uncovered PVT collectors. For full regeneration, the required usable roof area relative to the annual heat demand is about 1.8m2/MWh for PVT and 1.2m2/MWh for flat-plate collectors. Large buildings often do not provide sufficient roof area for full regeneration. A sustainable heat supply of the entire district with regenerated BHEs can be possible in suburban neighbourhoods, if the bigger buildings are distributed rather evenly. In urban neighbourhoods, areas may exist in which solar thermal regeneration alone is not sufficient.

Satellite imagery to select a sample of rooftops for a PV installation project in Jeddah, Saudi Arabia

A region-based convolutional neural network image segmentation approach (Mask R-CNN) was applied to identification of flat rooftops from satellite imagery in the city of Jeddah in Saudi Arabia. The model was trained on a small sample of rooftops (202) digitized from a 0.5 m resolution image (covering 0.21 km2) and then was applied to an independent area 4.5 km away. The precision and recall of the model were 0.98 and 0.96 respectively in terms of identifying rooftops in the independent area. A spatially stratified sample of rooftops was drawn from those identified by the model and the median roof area of the sample was not significantly different from the area as a whole. The results, although at a small scale, demonstrate the effectiveness of this approach for selecting buildings with appropriate rooftops for solar photovoltaic (PV) installation, in the context of closely spaced flat-roofed buildings, without requiring cadastral mapping or LIDAR datasets.

Procedure for the selection and evaluation of prefabricated housing buildings for the implementation of green roofs in the context of Urban Heat Island mitigation. The example of Wrocław, Poland

The assessment of the suitability of existing buildings for implementation of green roofs is an important research issue, especially in the context of Urban Heat Island (UHI), the negative impacts of which are locally exacerbated by the global warming. The studies carried out so far have covered a variety of buildings and have taken into account a range of different conditions. Relatively little attention has been paid to the possibilities of greening the roofs of prefabricated apartment blocks from the second half of the 20th century in the context of the potential climate effect. Yet, these buildings are found in many cities around the world, and seem in fact attractive for greening. In view of the above, we proposed a three-stage investigatory procedure to: (I) identify and classify buildings based on the number of floors and the rooftop available area; (II) select buildings by designating priority areas depending on the highest UHI intensity and roof density; (III) analyse the roof load capacity to develop retrofit scenarios. The procedure was applied to prefabricated housing estates built in the 1970s and 1980s in Wrocław, Poland. The research shows that there are 1962 buildings of different heights and roof area of 722405 m2, of which 480 buildings with a roof area of 122749.1 m2 were selected for greening within priority areas. The structure of the studied roofs was not designed to carry additional loads, which requires the application of complementary solutions. Scenario 1 assumes extensive greening provided that the existing ventilated roof is strengthened, scenario 2 –semi-intensive greening, which however requires the conversion of the ventilated roof to a non-ventilated one. The presented procedure can be applied in any other city with prefabricated apartment blocks and available UHI data, and serve to support the decision to implement green roofs to mitigate UHI.

Rainwater harvesting for fish farming water requirements in Boyolali Regency, Indonesia

<p>Climate change causes dry and rainy seasons to shift. Hydrology also shows that the number of rainwater changes with the uncertainty of its potential occurrence. In catfish farming and hydroponic farming, ensuring water availability is important for the sustainability of the project. Existing research is about managing rainwater, which can help partially supply water for both projects' benefit. The research location is in Jeron Village, Nogosari District, Boyolali Regency. Boyolali Regency is a 22 % residential area of the total area. It means 570 m³ potential storage. However, the result shows that only 17% of the roof area can collect rainfall for residential houses. In this study's residential case example, 17% of the roof area gives 97.8 m³ / year. The problem is that currently, there is no Rain Water Harvesting (RWH). A system capable of supporting the water supply. Using RWH provides a benefit based on the potential supply. This study highlights the potential benefits of using RWH. This pool yields a profit of up to (Indonesian Rupiah) IDR. 36,643,718 / month or IDR. 439,724.61 / year, with a probability of 80%. It means that in 5 years, it failed once. Moreover, water needs can supply from RWH.</p>

A Case Study on Reliability, Water Demand and Economic Analysis of Rainwater Harvesting in Australian Capital Cities

This paper presents reliability, water demand and economic analysis of rainwater harvesting (RWH) systems for eight Australian capital cities (Adelaide, Brisbane, Canberra, Darwin, Hobart, Melbourne, Perth and Sydney). A Python-based tool is developed based on a daily water balance modelling approach, which uses input data such as daily rainfall, roof area, overflow losses, daily water demand and first flush. Ten different tank volumes are considered (1, 3, 5, 10, 15, 20, 30, 50, 75 and 100 m3). It is found that for a large roof area and tank size, the reliability of RWH systems for toilet and laundry use is high, in the range of 80–100%. However, the reliability for irrigation use is highly variable across all the locations. For combined use, Adelaide shows the smallest reliability (38–49%), while Hobart demonstrates the highest reliability (61–77%). Furthermore, economic analysis demonstrates that in a few cases, benefit–cost ratio values greater than one can be achieved for the RWH systems. The findings of this study will help the Australian Federal Government to enhance RWH policy, programs and subsidy levels considering climate-sensitive inputs in the respective cities.

The effectiveness of dayligting through the toplighting design in large-volume building models

The utilization of daylighting as an effort to reduce the usage of building energy needs to be optimized. This is because, low-rise buildings with large volumes require specific strategies for proper light distribution throughout the space. Hence, this study aims to explore and compare the effectiveness of daylighting in the openings design of skylight, roof monitor, and sawtooth in large-volume buildings in Bandung. The effectiveness is assessed by examining lighting performance based on distribution patterns, the value of Daylight Factor (DF), and the Coefficient of Uniformity (CU). Experimental research method with simulation program known as Velux Daylight Visualizer 3.0 is used to present and analyze the lighting data. The results showed that the type, position (openings' height and distance), as well as the opening direction affected the distribution patterns and daylighting performance. Based on the simulation, the skylight opening is the most effective in meeting the standard value of Daylight Factor (> 2%) and Coefficient of Uniformity (CU min. 0.3 and DF min. 0.8%). Thus, space and openings model with a percentage roof area of 16.3% in the simulation can be used as a reference for large-volume buildings because, it meets the requirements of Daylight Factor value and sufficient Coefficient of Uniformity.

Degradation of Plasticized Poly(1-chloroethylene) Waterproofing Membranes used as a Building Material

In the present work, both unused plasticized poly(1-chloroethylene) membranes and membranes taken from a flat roof area were comprehensively analysed. First, tensile strength and elongation at breaking points were determined, followed by measurements of wettability. Secondly, morphological changes were analysed using scanning electron microscopy (SEM). To study chemical changes in aged membranes, Fourier transform infrared spectroscopy (FTIR) analysis in the attenuated total reflection mode (ATR) was used. Finally, thermogravimetric analysis and differential scanning calorimetry (TGA-DSC) were performed simultaneously to study thermal degradation. The results show obvious changes in the mechanical, physical and chemical properties of membranes caused by plasticizer loss. Surface microstructure becomes stiffer, which leads to contractions and the prevalence of voids. In cross-sectional area, average thickness values decrease. Due to the degradation of the plasticized waterproofing membranes, the roofing area had to be completely replaced.

Towards open-source LOD2 modelling using convolutional neural networks

The aim of this paper is to classify and segment roofs using vertical aerial imagery to generate three-dimensional (3D) models. Such models can be used, for example, to evaluate the rainfall runoff from properties for rainwater harvesting and in assessing solar energy and roof insulation options. Aerial orthophotos and building footprints are used to extract individual roofs and bounding boxes, which are then fed into one neural network for classification and then another for segmentation. The approach initially implements transfer learning on a pre-trained VGG16 model. The first step achieves an accuracy of 95.39% as well as a F1 score of 95%. The classified images are segmented using a fully convolutional network semantic segmentation model. The mask of the segmented roof planes is used to extract the coordinates of the roof edges and the nexus points using the Harris corner detector algorithm. The coordinates of the corners are then used to plot a 3D Level of Detail 2 (LOD2) representation of the building and the roof height is determined by calculating the maximum and minimum height of a Digital Surface Model LiDAR point cloud and known building height data. Subsequently the wireframe plot can be used to compute the roof area. This model achieved an accuracy of 80.2%, 96.1%, 96.0%, 85.1% and 91.1% for flat, hip, gable, cross-hip and mansard roofs, respectively.