The use of 3D GIS to analyse the influence of urban context on buildings’ solar energy potential

Machete, Rita; Falcão, Ana Paula; Gomes, M. Glória; Rodrigues, A. Moret

doi:10.1016/j.enbuild.2018.07.064

Cited by 65 publications

(30 citation statements)

References 18 publications

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“…The longwave heat flux component of the heat flux balance equation represents this heat flux exchange. The longwave heat flux component for the rooftop surface is defined as [39] q LW = q air + q sky (6) where, q sky and q air are heat fluxes due to exchange of radiation from sky and air. These heat flux components are dictated by the temperature difference of the bodies.…”

Section: Building Simulation and Analysismentioning

confidence: 99%

“…The European Union 2020 directive about building laws says that by 2020, buildings should consume 20% less electricity, emit 20% less GHG, and generate 20% onsite energy by using renewable resources [5]. The electricity consumption of buildings in the European Union countries is 40% of the total electricity requirements [6] while this share is 80% in Saudi Arabian buildings [7]. Although the country is committed to include a large share of solar energy-based power generation in its national grid [8], unfortunately, no attention has been given to transform buildings from just users of energy to producers of energy [9].…”

Section: Introductionmentioning

confidence: 99%

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Performance Enhancement of Roof-Mounted Photovoltaic System: Artificial Neural Network Optimization of Ground Coverage Ratio

Alghamdi

2021

Energies

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Buildings in hot climate areas are responsible for high energy consumption due to high cooling load requirements which lead to high greenhouse gas emissions. In order to curtail the stress on the national grid and reduce the atmospheric emissions, it is of prime importance that buildings produce their own onsite electrical energy using renewable energy resources. Photovoltaic (PV) technology is the most favorable option to produce onsite electricity in buildings. Installation of PV modules on the roof of the buildings in hot climate areas has a twofold advantage of acting as a shading device for the roof to reduce the cooling energy requirement of the building while producing electricity. A high ground coverage ratio provides more shading, but it decreases the efficiency of the PV system because of self-shading of the PV modules. The aim of this paper was to determine the optimal value of the ground coverage ratio which gives maximum overall performance of the roof-mounted PV system by considering roof surface shading and self-shading of the parallel PV modules. An unsupervised artificial neural network approach was implemented for Net levelized cost of energy (Net-LCOE) optimization. The gradient decent learning rule was used to optimize the network connection weights and the optimal ground coverage ratio was obtained. The proposed optimized roof-mounted PV system was shown to have many distinct performance advantages over a typical ground-mounted PV configuration such as 2.9% better capacity factor, 15.9% more energy yield, 40% high performance ratio, 14.4% less LCOE, and 18.6% shorter payback period. The research work validates that a roof-mounted PV system in a hot climate area is a very useful option to meet the energy demand of buildings.

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Section: Building Simulation and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Enhancement of Roof-Mounted Photovoltaic System: Artificial Neural Network Optimization of Ground Coverage Ratio

Alghamdi

2021

Energies

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“…The creation of a 3D city block model in GIS required the collection of geospatial data that contain information on the location and footprint of the buildings. The main sources of geospatial data are topographical maps showing the boundaries of properties in relation to adjoining properties and geographic features [31]. Additional sources for collecting heights of the geographic features are AHN3 point cloud data, which are created with satellite remote sensors and photogrammetry methods.…”

Section: Building Heat Demand Modellingmentioning

confidence: 99%

A Decision Support Tool for Implementing District Heating in Existing Cities, Focusing on Using a Geothermal Source

2020

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In the context of climate change mitigation strategies in urban environments and reducing reliance on carbon-based energy sources, the Netherlands is gradually taking steps towards modification of its thermal energy system. Geothermal energy, widely used in agriculture, has recently emerged as a local, clean, and sustainable energy source able to fulfil the residential demand for space heating and has received growing attention in district energy planning. However, failed attempts in the past and the lack of experience with direct application of this technology in district heating systems has increased uncertainty with respect to the technical, spatial, and socioeconomic barriers to be overcome between supply and demand. This calls for the application of decision support tools in order to remove these barriers and facilitate more appropriate decision making towards the implementation of smart thermal energy grids in local energy communities. This research explores how the network of actors, those who see opportunities in direct use of geothermal energy in the Netherlands, can work on the transformation of the current centralised carbon-based energy systems towards local sustainable energy communities.

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“…Considering the general characteristics of the buildings, it is possible to estimate the number of roofs of different types, determine the parameters which are needed to determine solar PV capacity potential. In order to evaluate building roofs at the city scale, it is fundamental to have a 3D urban model with LiDAR point cloud data [34]. However, an important aspect to consider is that the extent of the studied area is sometimes limited by the available data [33,35].…”

Section: Literature Review Of Related Workmentioning

confidence: 99%

Potential analysis of roof-mounted solar photovoltaics in Sweden

et al. 2020

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A comprehensive analysis framework for roof-mounted solar PV systems is developed.• The estimated roof area for Västerås municipality is 5.74 km 2 .• Different scenarios are considered for the potential installation of PV systems.• The potential capacity is 727-956 MWp and annual yield is 626-801 GWh for Västerås.• 504 km 2 usable roof area and 65-84 GWp installed capacity are estimated for Sweden.

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The use of 3D GIS to analyse the influence of urban context on buildings’ solar energy potential

Cited by 65 publications

References 18 publications

Performance Enhancement of Roof-Mounted Photovoltaic System: Artificial Neural Network Optimization of Ground Coverage Ratio

Performance Enhancement of Roof-Mounted Photovoltaic System: Artificial Neural Network Optimization of Ground Coverage Ratio

A Decision Support Tool for Implementing District Heating in Existing Cities, Focusing on Using a Geothermal Source

Potential analysis of roof-mounted solar photovoltaics in Sweden

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