Rooftop Of Building Research Articles

Increasing the deployment of Solar PV in the commercial sector in South Africa: Buildings as assets for energy transition

South African electricity system is undergoing energy transition towards increased use of renewable energy. The share of coal generated power was 83% in 2022 versus 94% a decade ago and 8% of electricity was generated with renewables. At large utility scale, the integrated resource planning (IRP) process is a policy vehicle used to increase renewable energy penetration within the grid. However, at distributed and small-scale level, the process of renewable energy uptake is heterogenous, complex, user-centric and relies on customer preferences, cost, the enabling municipal regulations and tariffs. This dynamic adoption makes planning for distributed energy systems very complex as energy planning tools are designed for centralised planning. To overcome this planning challenge, this paper adopts a three-step modelling process to estimate rooftop solar PV adoption for commercial buildings. This modelling process combines optimisation, market research and simulation modelling approaches using availability of commercial building rooftops, rooftop suitability properties and solar PV resource as constraints. With excellent solar PV resource potential in the country, and at high rooftop suitability levels of 80%, up to 12 GW of solar PV rooftops can be installed on commercial buildings. Therefore, commercial buildings can serve as strategic assets for decarbonisation and increasing the security of power supply.

A Study on the Indoor Heating Effect of Blue Green Roofs during Cold Waves

With the increasing frequency of heat-based climate disasters due to recent climate change, various technologies are being developed to mitigate their impacts. Blue-green roofs (BGR) is a technology that consists of rainwater storage tanks and grass layers installed on building rooftops to reduce the infiltration of cold or heat into the building interiors. This study analyzed the indoor temperature changes between two buildings with equal rooftop area, one equipped with a BGR and the other without, during winter season to evaluate their heating effects. The results indicated an average heating effect of 0.56 °C across the entire monitoring period for the building with a BGR. The heating effect was observed only at temperature ranges below 6.5 °C, with no effect at higher temperatures. This study suggests that even installing a roof-based system can provide some degree of heating benefits. Further research and development are needed to improve the heating efficien through additional technological advancements.

The Contribution of Green Roofs in the Achievement of Sustainable Development Goals

Green roofs are eco-friendly systems designed to deliver environmental, economic and social benefits while they enhance the aesthetic appeal of buildings. Their construction on rooftop of buildings in urban communities is important for the local residents and the environment. The Sustainable Development Goals are a set of 17 global objectives established by the United Nations in 2015 serving as a blueprint for developing a more sustainable, equitable and prosperous world by 2030, focusing on environmental, economic and social progress. The multiple benefits of green roofs are related with the achievement of the sustainable development goals. Creation of green roofs in urban areas facilitates the achievement of the SDG3 related with good health and well-being as well as SDG11 related with sustainable cities and communities, SDG13 related with climate action and SDG15 related with life on land. Therefore, the development of green roofs in urban areas is very useful and beneficial for the buildings’ residents, the environment and the local community. Due to multiple external benefits of green roofs their construction should be promoted with financial and non-financial incentives by municipal and regional authorities combined with removal of several barriers hindering their adoption and development.

Building Energy Savings and Power Output Augmentation of Roof Mounted PV Using Co-Located Rooftop Reflectors

Abstract Photovoltaic panels (PVs) installed on building rooftops yield a positive influence on the thermal performance of the building due to the shading of the PV panels, decreasing cooling loads while causing a smaller increase in heating loads. Additionally, the electrical power output of PV panels has been shown to be increased by including reflectors between PV rows, concentrating the solar flux onto the active portion of the panels. When implemented into the spaces between the rows of a roof-mounted PV array, reflectors might further improve the positive thermal effects of rooftop installed PV arrays. This work focuses on predicting rooftop heat flux and temperature for a building rooftop equipped with PV panels and reflectors. The Saved Energy Load (SEL), Additional Energy Load (AEL), PV power output, rooftop heat flux and the utility factor (ratio of positive building energy impacts to negative building energy impacts) are reported parametrically for variations in the rooftop absorptivity and reflector area for three United States locations. Utility factors of 375, 140 and 160 are found for Phoenix, Boise and Dayton, respectively, for a reflector covering the full area between panels with a roof having a minimal absorptivity. A building in Phoenix exhibits a 15% increase in the utility factor of the PV-building system when reflectors are incorporated compared against a PV-building system without reflectors, while a building in Dayton showed a 22% increase in utility factor when reflectors are included between the rows of a roof-mounted PV array.

Modeling building energy self-sufficiency of using rooftop photovoltaics on an urban scale

The significant contribution of buildings to global energy-related CO2 emissions and climate change has led to projections of a carbon–neutral building stock by 2050. This study evaluates the potential contribution of rooftop photovoltaics to urban energy self-sufficiency by developing an enhanced CityBEM framework, our in-house urban building energy model (UBEM). This methodology enables city-scale, simultaneous simulations of building energy use and rooftop photovoltaic retrofitting with low computational time. CityBEM’s robustness and high spatiotemporal resolution facilitate transient simulations of individual buildings with diverse usage types across large urban areas, addressing common computational constraints and input data limitations in UBEM applications. The rooftop photovoltaic model in CityBEM utilizes a comprehensive approach with physics-based modeling of crystalline PVs, model validation, and a proper design of array networks to manage self-shading effects. More than 57,000 buildings with a combined footprint of 32.37 million square meters are located in the simulation test case covering downtown Montreal (Quebec, Canada) and the surrounding areas. Results indicate that rooftop PV adoption in these buildings could potentially generate 5.9 TWh of electricity annually, reduce energy demand by 27.3%, and achieve an average annual energy saving of 40%. This local generation could also reduce operational CO2 equivalent emissions by over 0.2 megatons. Overall, this study highlights the significant potential of rooftop photovoltaics for a cleaner, more energy self-sufficient urban future.

SolarSAM: Building-scale Photovoltaic Potential Assessment Based on Segment Anything Model (SAM) and Remote Sensing for Emerging City

Driven by advancements in photovoltaic (PV) technology, solar energy has emerged as a promising renewable energy source due to its ease of integration onto building rooftops, facades, and windows. For emerging cities, the lack of detailed street-level data presents a challenge for effectively assessing the potential of building-integrated photovoltaic (BIPV). To address this, this study introduces SolarSAM, a novel BIPV evaluation method that leverages satellite imagery and deep learning techniques, and an emerging city in northern China is utilized to validate the model performance. SolarSAM segmented various building rooftops using text prompt-guided semantic segmentation during the process. Separate PV models were then developed for Rooftop PV, Facade-integrated PV, and PV windows, using this segmented data and local climate information. The potential for BIPV installation, solar power generation, and city-wide power self-sufficiency were assessed, revealing that the annual BIPV power generation potential surpassed the city’s total electricity consumption by a factor of 2.5. Economic and environmental analysis were also conducted for the BIPVs on different buildings; the levelized cost of electricity is 0.18-0.41 CNY/kWh, and the annual total carbon reduction is 7.08×107 T CO2. These findings demonstrated the model’s performance and revealed the potential for BIPV power generation.

Exploring the climatic conditions effect on spatial urban photovoltaic systems development using a spatial multi-criteria decision analysis: A multi-city analysis

Identifying suitable locations for urban photovoltaic systems (UPVS) is crucial for achieving sustainable energy objectives and designing smart, eco-friendly cities. This study assesses the potential for UPVS expansion in eight cities across different climatic zones in Iran using a spatial multi-criteria decision-making method. Two scenarios were analyzed: the first compared spatial potential within each city, and the second compared potential between cities. The findings indicate that rooftops of the tallest buildings in densely populated areas, especially those with high solar energy output and sky view factor, hold the greatest potential for UPVS development. These locations are often near parks, commercial centers, and road networks. In the first scenario, Ardabil (5.70%), Gorgan (4.65%), Mashhad (5.46%), Tehran (8.10%), Kermanshah (5.76%), Shahrekord (3.41%), Kerman (8.67%), and Zahedan (8.56%) show significant potential for photovoltaic development. In the second scenario, cities in hot, dry climates like Zahedan and Kerman exhibit greater potential compared to cities in moderate, humid climates like Ardabil and Gorgan. Based on the analysis of this scenario, Ardabil (0.04%), Gorgan (1.49%), Mashhad (5.58%), Tehran (5.06%), Kermanshah (0.00%), Shaherkord (0.03%), Kerman (21.70%) and Zahedan (39.11%) showed a very high potential for UPVS development. The results of this study offer valuable insights for urban solar energy planning.

Review of the Benefits of Green Roofs

Green roofs installed on rooftop of buildings in urban communities have multiple environmental, economic, energy and social benefits. They promote the urban sustainability and the well-being of the local residents. The impact of green roofs in buildings, in the environment and in local communities have been studied. Green roofs remove atmospheric pollutants including carbon dioxide, release oxygen, mitigate the urban heat island effect, reduce the energy consumption in buildings, improve the rainwater management, promote urban agriculture, enhance urban green spaces and bring nature closer to local residents. The development of green roofs results in many benefits in the buildings that have been constructed as well as in the broader community. Their construction is financially subsidized in several countries due to their multiple external benefits. In the era of climate change and of sustainable development construction of green roofs in urban environments has multiple positive impacts. Therefore, local and municipal authorities should promote their construction in public and private buildings. The current work emphasizes the benefits of green roofs and it could be useful to policy makers, to public and municipal authorities as well as to architects, construction companies and buildings’ owners who should promote their development in urban communities.

Rooftop PV Potential Determined by Backward Ray Tracing: A Case Study for the German Regions of Berlin, Cologne, and Hanover

ABSTRACTPhotovoltaics (PV) on building rooftops is a major contributor to the decarbonization of energy systems. We simulate the PV energy yield potential for 2.5 million individual roofs in three German regions. We cumulate the results for each single roof to calculate the cost‐potential curves for the three cities Berlin, Cologne, and Hanover. These curves give the amount of electricity that can be generated at less than a given cost per kWh. We find that these curves have the shape of a hockey stick. Neglecting the dependence of PV investment on building size and thus on the system sizes causes largely different cost‐potential curves that differ by 11%–18% for flat roofs due to their heterogeneous building size distribution. The cost‐potential curves of the three cities are very similar when appropriately normalized, for example, by the local solar irradiation and the settlement area of the city, despite substantial variations in population density. This allows for an extrapolation of our results. For Germany, we reveal an upper limit for the total electricity generation from rooftop PV of 762 TWh/a with cost as low as 6.9 ct/kWh without accounting for area losses due to chimneys, air conditioning systems, and so forth. We estimate the actual potential to be at least half of that figure.

Energy harvesting using rooftops in urban areas: Estimating the electricity generation potential of PV and wind turbines considering the surrounding environment

Building rooftops, rich in sunlight and with high wind speeds, are among the most ideal locations for installing renewable energy systems (RES) in urban areas. However, not all rooftops are suitable for RES installation. Therefore, this study aimed to maximize energy harvesting using limited rooftop space. To this end, four scenarios were established based on a district in Seoul, South Korea, and the annual electricity generation of the RES was then estimated. This process was refined further into seven scenarios to assess economic feasibility. The principal findings of this research are as follows. First, depending on the type of RES installed on rooftops, it is possible to generate between 755,124 kWh and 3,236,918.3 kWh of electricity annually. Second, in terms of economic feasibility, scenarios predominantly featuring photovoltaic (PV) installations proved economically viable, whereas those mainly involving vertical axis wind turbines (VAWTs) did not guarantee economic feasibility. Third, for VAWTs scenarios with lower economic viability, adjusting the Renewable Energy Certificate weight and providing government subsidies up to USD1,131/kW could improve the profitability. These results provide a foundation for RES operators to design appropriate projects based on economic evaluations, and for policymakers to formulate effective policies to better promote the diffusion of RES.

Performance prediction of ventilated building-integrated photovoltaic system with lightweight flexible crystalline silicon module based on experimental fitting method

The novel ventilated building-integrated photovoltaic system with lightweight flexible crystalline silicon modules (VL-BIPV) has a self-weight of only about 6 kg/m2, which helps to address weight-bearing challenges on low-capacity industrial building rooftops. However, the unique thermal dissipation features of the system pose challenges for the analysis of its photovoltaic performance and thermal processes. Therefore, a comparative experimental testing approach was employed, comparing the VL-BIPV system with a conventional rooftop photovoltaic system. Using the efficiency equations of the conventional photovoltaic system as a reference, the functional equations were fitted based on experimental results. Based on typical meteorological year data in Nanjing, annual power generation and PV cell temperature variations were evaluated, along with power generation and carbon reduction benefits over a 25-year lifetime. The results indicate that the VL-BIPV system achieves an annual power generation of 262.22 kWh/m2, a 6.52% increase compared to the conventional system. Additionally, the highest average and maximum cell temperatures in the VL-BIPV system during the year are 58.39 °C and 64.74 °C, respectively, both lower than the conventional system's peak at 68.34 °C. Over a 25-year lifespan, the VL-BIPV system reduces carbon emissions by 20.17 kgCO2/Wp, exceeding the conventional system's reduction by 1.25 kgCO2/Wp.

Scheduling optimization of shared energy storage and peer-to-peer power trading among industrial buildings

Constructing distributed photovoltaic systems on industrial building rooftops and establishing adaptive microgrid energy flow scheduling models are effective means of achieving full deployment and consumption of photovoltaics. The operational modes and stakeholders involved in shared energy storage and peer-to-peer trading differ significantly, influencing both the energy flow scheduling and on-site consumption rates of microgrids. In this study, a dual-objective function model with multiple constraints was designed, and particle swarm optimization was applied to seek optimal energy storage capacity. Pareto optimal solution sets under dual objectives with conflicting interests were explored, and comparative analyses of energy flow scheduling under shared energy storage and peer-to-peer trading microgrid modes were conducted. Furthermore, the daily cost-effectiveness of these two modes was evaluated. The results indicated that stakeholders with conflicting interests difficult to achieve win–win outcomes in energy scheduling. The integration of peer-to-peer trading not only reduced shared energy storage capacity by 18% but also achieved local consumption rates of 62% and 100% in summer and winter, respectively. Peer-to-peer trading had a minimal impact on user power costs, yet it increases power revenues by 32% and 235% in summer and winter, respectively, thereby reducing the net expenditure of the users. The results of this study provide a reference for microgrid energy flow scheduling models involving entities of uncommon interest in industrial parks.

Indicators for suitability and feasibility assessment of flexible energy resources

Recommender systems play a critical role in optimizing building energy consumption by providing personalized advice based on data analytics and user preferences. However, the literature highlights the need for systems that can justify their recommendations, as many of these systems use non-transparent machine-learning techniques. This research introduces two distinct types of indicators with three main goals: to identify patterns of flexible consumption behavior using transparent and straightforward methods suitable for remote decision support systems, thereby eliminating the need for extensive databases; to evaluate the feasibility of installing solar panels on building facades, rooftops, and structures using high-resolution 3D models; and to enhance understanding through a quantitative assessment of the feasibility and suitability of integrating renewable energy sources, particularly photovoltaic systems. Flexible prosumers are scored by assessing their energy consumption level, consistency, and variability through the Flexible Consumption Indicators. Topology Indicators perform a quantitative assessment of the feasibility of support surfaces for installing photovoltaic panels, taking into account rooftop pitch angles, orientations, and surrounding and internal structures, identifying those areas exposed to sufficient levels of irradiation. This study, which uses actual consumption profiles and similar households' buildings 3D models, demonstrates how the proposed indicators can aid identifying users with flexible consumption profiles that reside in buildings compatible with renewable energy sources, aiding in decision-making process within the energy transition.

Towards Auto-Calibrated UBEM Using Readily Available, Underutilized Urban Data: A Case Study for Ithaca, NY

The shift toward urban decarbonization, which involves transitioning to heat pumps and integrating photovoltaic (PV) energy generation, poses substantial challenges to electricity grids in cities. Ithaca, NY, with its commitment to the Green New Deal, serves as a case study for cities confronted with these challenges. This paper explores how various building electrification scenarios might affect Ithaca's net-zero ambitions. We show that our reduced-order Urban Building Energy Modeling (UBEM) approach can accurately predict building energy consumption and rooftop solar PV energy generation for the 6114 buildings in the City of Ithaca. Building on that, we present three increasingly ambitious decarbonization scenarios that offer a holistic perspective on Ithaca's future electrification potential. In automating the reconstruction of building geometry from readily available LiDAR data, along with automated BEM setup and calibration, our methodology offers a robust toolkit to plan holistic decarbonization efforts. We argue that our approach, which forecasts both UBEM and PV for all residential and commercial buildings within a single framework, provides insights that cannot be reached with siloed methods. In the future, we believe that the abundance of readily available urban data sets will allow us to transition our approach to other jurisdictions across the US.

Multi-Scenario analysis of rooftop greening regulation on runoff effects based on adaptive Evaluation: A case study of Macau, China

The reduction in internal ecological spaces within urban environments and the expansion of impervious surfaces have been identified as factors significantly increasing the susceptibility of cities to flood disasters. Rooftop greening, an essential component of urban stormwater management strategies, offers a versatile solution for augmenting permeable surfaces and delivering ecological services while seamlessly integrating with existing urban infrastructure. This approach has been proven effective in enhancing urban resilience against rainfall-induced flooding events. Hence, the precise identification of suitable building rooftops for green infrastructure within urbanized regions, combined with the simulation of runoff regulation effects brought about by these rooftop greening interventions, holds substantial practical significance. This study innovatively constructs a technical method for measuring the runoff control performance of urban-scale green roofs from identification, evaluation to simulation. Taking the Macao Special Administrative Region as an example, a green roof suitability evaluation index system that is consistent with urban characteristics has been constructed, integrating machine learning, comprehensive evaluation, SWMM simulation methods to evaluate the suitability of roof greening in urban areas and simulate the impact of multi-senarios green roof construction types on runoff regulation. Finally, a comprehensive strategy for rooftop greening construction in Macau is proposed, with the aim of amplifying the runoff regulation capabilities of such initiatives. The main findings of the research include the following: (1) The research area exhibits a notably high aggregate potential for rooftop greening, with suitable rooftop areas comprising approximately 34% of the total rooftop expanse. Notably, construction vintage and roof elevation are identified as the most substantive constraints on rooftop greening. (2) Discernible differences in rooftop greening construction scale exert a significant influence on runoff regulation capacity, with the magnitude of greening positively correlated with its runoff regulation efficacy. Distinct greening typologies elicit varying impacts on runoff regulation, with semi-intensive rooftop greening surpassing extensive approaches in runoff regulation performance. Disparate distribution patterns of rooftop greening engender dissimilar runoff regulation effects, with rooftop greening layouts situated distant from drainage outlets facilitating runoff mitigation, while those in close proximity to drainage outlets enhance peak flow reduction effects.

Experimental study on rooftop flow field of building based on the operation of vertical-axis wind turbines

Vertical-axis wind turbines (VAWTs) demonstrate good adaptability for harnessing wind energy on the building rooftops. However, knowledge gaps still exist in the understanding of the wind resources on the rooftop, the operation of VAWTs, and its effect on the wind field. To add new knowledge on this subject, the operation of wind turbines modeled by the NACA0018 airfoil on the rooftop of a building is experimentally studied via wind tunnel. The results indicate that flow above the rooftop of building shows an obvious speed reduction due to the blunt body effect of the turbines, and the turbulence intensities are dramatically enhanced. The different tip-speed ratios, wind directions, and installation locations of wind turbine have significant effect on flow field on the roof. Due to the flow characteristics above the rooftop, the power spectral density of the fluctuating wind speed exhibits high energy below the top position of the VAWT on the rooftop. Additionally, the wind profiles and probability distribution of the wake of VAWTs on the rooftop are analyzed and mathematically fitted for quantifying wind field characteristics rooftop turbines on the roof. For the VAWTs investigated in this study, the impacts of the operation of VAWTs on the wind field are non-negligible on the roof of building. The findings of this study will provide valuable insights for the optimal placement of VAWTs and utilization of wind energy on the rooftops.

PromptNet: Prompt Learning for Roof Photovoltaic Potential Assessment

An increasing number of works have been proposed to use remote sensing images to assess the potential for rooftop Photovoltaic (PV) energy development in buildings. However, most methods focus mainly on the remote sensing images themselves, ignoring the key prior information of building type. Thus most works with Deeplabv3+ as backbone present suboptimal performance. To overcome this challenge, we propose a novel approach PromptNet that embeds the building types as prior knowledge and feed it into prompt learning for predict roof PV energy Potential. Specifically, a pre-trained semantic segmentation network, Deeplabv3+, is first constructed to detect potential building rooftops from remote sensing images. Then, the buildings are categorized into five types based on their functions, including government buildings, public buildings, industrial and commercial factories, agricultural housing, and other building types. Finally, by using prompt learning, the prior knowledge of buildings is established and associated with the rooftops that are suitable for PV energy development. This is embedded into a deep learning network, filtering out unsuitable rooftops, and significantly improving the accuracy of rooftop PV energy development. Comprehensive experiments show that the proposed method achieves 81.18% accuracy and 76.90% IOU in predicting the potential for rooftop PV energy, a 10.97% improvement in IoU compared to the backbone without prior knowledge.

Energy Conservation in Institutional Buildings: A Case Study in India

Abstract: Energy consumption is an important aspect with respect to operating costs of buildings. Modern buildings are being designed considering their energy performance and carbon footprints. However, there is scope to reduce the energyconsumption and carbon footprints in existing old buildings by suitable retrofitting. A case study is discussed where in the energy audit of an academic institution in northern India revealed several opportunities where energy consumption can be reduced considerably. This paper presents the analysis to achieve about 33% reductions in energy consumption and around 178 tons reduction in carbon footprint in an institutional building, Govt. Girls College Buildings Village Palwal Kurukshetra, India. It recommends measures that can be implemented with moderate investments and payback period. Another important feature is the utilization of roof-tops of institute building to install solar PV power plants apart from retrofitting measures. Presently 7% energy requirement is being met through installed solar PV power plant on the rooftop of institute building. Afterinstalling such power plants at all the roof-tops of building about 25% of energy needs can be met by solar energy source.

RANCANG BANGUN GENERATOR MAGNET PERMANEN FLUKS AKSIAL PADA PROTOTYPE PEMBANGKIT LISTRIK TENAGA BAYU (PLTB) SUMBU VERTIKAL

Axial flux permanent magnet generators are easy to manufacture because only the rotor and stator are aligned. Axial flux generators are one of the right choices for wind power plants due to the large number of magnetic poles, high electrical power density, and easy-to-increase power capacity. The axial flux type permanent magnet generator is designed with a speed of 600 rpm, a frequency of 50 Hz, a voltage of 12 V, a power of 7 Watts, 1 phase, 10 pairs of Neodymium Iron Boron (NdFeB) type magnetic poles, and 5 copper wire coils measuring 0.6 mm in diameter with a total of 301 turns. This vertical-axis wind farm test uses an axial flux permanent magnet generator with a 5? resistor load and is assisted by a blower. The blower is used to get a constant turbine rotation in accordance with the results of wind speed measurements taken for 3 days on the rooftop of the electrical engineering lecture building, namely the wind speed range of 3-5 m/s. Based on these tests, the generator is able to produce a voltage of 1.5 V, a current of 0.2 A, a power of 0.27 Watt, and a generator efficiency level of 0.63% at a wind speed of 3 m/s. It also produces a voltage of 4.4 V, a current of 0.7 A, a power of 2.77 Watt, and a generator efficiency level of 0.97% at a wind speed of 5 m/s.

Assessment of renewable power generation applied in homestay hotels: Energy and cost-benefit considering dynamic occupancy rates and reservation prices

Increasing the revenue from hotel operations and decreasing the stress on utility grids from hotel energy consumption are two priorities for the modern tourism industry. Dynamic occupancy rate and hotel reservation prices influence their operations revenue, while occupancy rate also impacts real hotel energy consumption. Renewable power generation as an effective measure can benefit hotels not only by decreasing electricity purchases but also increasing some extra income from selling electricity generated from renewable sources. This study investigated the effects of renewable power generation on energy and the cost-benefit performance of hotel operation prices under different occupancy rates and reservation prices. A two-stage energy and cost-benefit performance assessment and a novel comprehensive cost-benefit assessment model were developed to examine these research issues. The impact of the renewable power generation system (i.e., a photovoltaic-battery system installed on the building rooftop) was considered on the hotel energy and cost-benefit performance. It was found that the novel cost-benefit assessment model accurately quantified the overall hotel profit by calculating its energy consumption cost with the PV-battery system under different occupancy rates and reservation prices. Three typical homestay hotels located in Sanya were tested and assessed. The results showed that PV-battery systems effectively decreased the amount of electricity purchased by hotels. Besides, the renewable energy penetration was above 57.98% in various cases. The overall hotel profit increased by a maximum of 31.4% since renewable power generation was used under different occupancy rates and hotel reservation prices.