Search Results (284)

The transition to smart, zero-carbon cities relies on advanced, sustainable energy solutions, with artificial intelligence (AI) playing a crucial role in optimizing renewable energy management. This study evaluates state-of-the-art AI models for solar power forecasting, emphasizing accuracy, reliability, and environmental sustainability. Using operational data from Benban Solar Park in Egypt and Sakaka Solar Power Plant in Saudi Arabia, two of the world’s largest solar installations, the research highlights the effectiveness of hybrid AI techniques. The hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model outperformed other models, achieving a Mean Absolute Percentage Error (MAPE) of 2.04%, Root Mean Square Error (RMSE) of 184, Mean Absolute Error (MAE) of 252, and R² of 0.99 for Benban, and an MAPE of 2.00%, RMSE of 190, MAE of 255, and R² of 0.98 for Sakaka. This model excels at capturing complex spatiotemporal patterns in solar data while maintaining low computational CO₂ emissions, supporting sustainable AI practices. The findings demonstrate the potential of hybrid AI models to enhance the accuracy and sustainability of solar power forecasting, thereby contributing to efficient, resilient, and zero-carbon urban environments. This research provides valuable insights for policymakers and stakeholders aiming to advance smart energy infrastructure. Full article

To sustainably power future urban communities, cities require advanced solar energy planning tools that overcome the limitations of traditional approaches, such as data fragmentation and siloed decision-making. SDTs present a transformative opportunity by enabling precision urban modelling, integrated simulations, and iterative decision support. However, their application in solar energy planning remains underexplored. This study introduces SDT4Solar, a novel SDT-based framework designed to integrate city-scale rooftop solar planning through 3D building semantisation, solar modelling, and a unified geospatial database. By leveraging advanced spatial modelling and Internet of Things (IoT) technologies, SDT4Solar facilitates high-resolution 3D solar potential simulations, improving the accuracy and equity of solar infrastructure deployment. We demonstrate the framework through a proof-of-concept implementation in Ballarat East, Victoria, Australia, structured in four key stages: (a) spatial representation of the urban built environment, (b) integration of multi-source datasets into a unified geospatial database, (c) rooftop solar potential modelling using 3D simulation tools, and (d) dynamic visualization and analysis in a testbed environment. Results highlight SDT4Solar’s effectiveness in enabling data-driven, spatially explicit decision-making for rooftop PV deployment. This work advances the role of SDTs in urban energy transitions, demonstrating their potential to optimise efficiency in solar infrastructure planning. Full article

Boda boda motorbike taxis are a primary mode of transport in Nairobi, Kenya, and a major source of urban air pollution. This study investigates the environmental and electrical grid impacts of electrifying Nairobi’s boda boda fleet. Using real-world tracking data from 118 motorbikes, we simulated the effects of a full-scale transition from internal combustion engine (ICE) vehicles to electric motorbikes. We analysed various scenarios, including different battery charging strategies (swapping and home charging), motor efficiencies, battery capacities, charging rates, and the potential for solar power offsetting. The results indicate that electrification could reduce daily ${\mathrm{CO}}_2$ emissions by approximately 85% and eliminate tailpipe particulate matter emissions. However, transitioning the entire country’s fleet would increase the national daily energy demand by up to 6.85 GWh and could introduce peak grid loads as high as 2.40 GW, depending on the charging approach and vehicle efficiency. Battery swapping was found to distribute the grid load more evenly and better complement solar power integration compared to home charging, which concentrates demand in the evening. This research provides a scalable, data-driven framework for policymakers to assess the impacts of transport electrification in similar urban contexts, highlighting the critical trade-offs between environmental benefits and grid infrastructure requirements. Full article

Distributed photovoltaic (PV) generation is increasingly important for urban energy systems amid global climate change and the shift to renewable energy. Traditional PV deployment prioritizes maximizing energy output, often neglecting electricity price variability caused by time-of-use tariffs. This study develops a high-resolution planning and economic assessment model for building-integrated PV (BIPV) systems, incorporating hourly electricity real-time market prices, solar geometry, and submeter building spatial data. Wuhan (30.60° N, 114.05° E) serves as the case study to evaluate optimal PV placement and tilt angles on rooftops and façades, focusing on maximizing economic returns rather than energy production alone. The results indicate that adjusting rooftop PV tilt from a maximum generation angle (30°) to a maximum revenue angle (15°) slightly lowers generation but increases revenue, with west-facing orientations further improving returns by aligning output with peak electricity prices. For façades, south-facing panels yielded the highest output, while north-facing panels with tilt angles above 20° also showed significant potential. Façade PV systems demonstrated substantially higher generation potential—about 5 to 15 times that of rooftop PV systems under certain conditions. This model provides a spatially detailed, market-responsive framework supporting sustainable urban energy planning, quantifying economic and environmental benefits, and aligning with integrated approaches to urban sustainability. Full article

Accurate estimation of available rooftop areas for PV power generation at the city scale is critical for sustainable energy planning and policy development. In this study, using publicly available high-resolution satellite imagery, rooftop solar energy potential in urban, rural, and industrial areas is estimated using deep learning models. In order to identify roof areas, high-resolution open-source images were manually labeled, and the training dataset was trained with DeepLabv3+ architecture. The developed model performed roof area detection with high accuracy. Model outputs are integrated with a user-friendly interface for economic analysis such as cost, profitability, and amortization period. This interface automatically detects roof regions in the bird’s-eye -view images uploaded by users, calculates the total roof area, and classifies according to the potential of the area. The system, which is applied in 81 provinces of Turkey, provides sustainable energy projections such as PV installed capacity, installation cost, annual energy production, energy sales revenue, and amortization period depending on the panel type and region selection. This integrated system consists of a deep learning model that can extract the rooftop area with high accuracy and a user interface that automatically calculates all parameters related to PV installation for energy users. The results show that the DeepLabv3+ architecture and the Adam optimization algorithm provide superior performance in roof area estimation with accuracy between 67.21% and 99.27% and loss rates between 0.6% and 0.025%. Tests on 100 different regions yielded a maximum roof estimation accuracy IoU of 84.84% and an average of 77.11%. In the economic analysis, the amortization period reaches the lowest value of 4.5 years in high-density roof regions where polycrystalline panels are used, while this period increases up to 7.8 years for thin-film panels. In conclusion, this study presents an interactive user interface integrated with a deep learning model capable of high-accuracy rooftop area detection, enabling the assessment of sustainable PV energy potential at the city scale and easy economic analysis. This approach is a valuable tool for planning and decision support systems in the integration of renewable energy sources. Full article

This study evaluates the feasibility and visibility of electric vehicles (EVs) in Egypt, addressing critical research gaps and proposing actionable strategies to drive adoption. Employing a systematic review of academic, governmental, and industry sources, the paper identifies underexplored areas such as rural–urban adoption disparities, lifecycle assessments of EV batteries, and sociocultural barriers, including gender dynamics and entrenched consumer preferences. Its primary contribution is an interdisciplinary framework that integrates technical aspects, such as grid resilience and climate-related battery degradation, with socioeconomic dimensions, providing a holistic overview of EV feasibility in Egypt tailored to Egypt’s context. Key findings reveal infrastructure limitations, inconsistent policy frameworks, and behavioral skepticism as major hurdles, and highlight the untapped potential of renewable energy integration, particularly through synergies between solar PV generation (e.g., Benban Solar Park) and EV charging infrastructure. Recommendations prioritize policy reforms (e.g., tax incentives, streamlined tariffs), solar-powered charging infrastructure expansion, public awareness campaigns, and local EV manufacturing to stimulate economic growth. The study underscores the urgency of stakeholder collaboration to transform EVs into a mainstream solution, positioning Egypt as a regional leader in sustainable mobility and equitable development. Full article

Dark asphalt surfaces, absorbing about 95% of solar radiation and warming to 60–70 °C during summer, intensify urban heat while providing substantial prospects for energy extraction. This review evaluates four primary technologies—asphalt solar collectors (ASCs, including phase change material (PCM) integration), photovoltaic (PV) systems, vibration-based harvesting, thermoelectric generators (TEGs)—focusing on their principles, efficiencies, and urban applications. ASCs achieve up to 30% efficiency with a 150–300 W/m² output, reducing pavement temperatures by 0.5–3.2 °C, while PV pavements yield 42–49% efficiency, generating 245 kWh/m² and lowering temperatures by an average of 6.4 °C. Piezoelectric transducers produce 50.41 mW under traffic loads, and TEGs deliver 0.3–5.0 W with a 23 °C gradient. Applications include powering sensors, streetlights, and de-icing systems, with ASCs extending pavement life by 3 years. Hybrid systems, like PV/T, achieve 37.31% efficiency, enhancing UHI mitigation and emissions reduction. Economically, ASCs offer a 5-year payback period with a USD 3000 net present value, though PV and piezoelectric systems face cost and durability challenges. Environmental benefits include 30–40% heat retention for winter use and 17% increased PV self-use with EV integration. Despite significant potential, high costs and scalability issues hinder adoption. Future research should optimize designs, develop adaptive materials, and validate systems under real-world conditions to advance sustainable urban infrastructure. Full article

Accurate assessment of urban rooftop solar photovoltaic (PV) potential is critical for the low-carbon energy transition. This study presents a deep learning-based approach using high-resolution (0.5 m) aerial imagery to automatically identify building rooftops in the central urban area of Wuhan, China (covering seven districts), and to estimate their PV installation potential. Two state-of-the-art semantic segmentation models (DeepLabv3+ and U-Net) were trained and evaluated on a local rooftop dataset; U-Net with a ResNet50 backbone achieved the best performance with an overall segmentation accuracy of ~94%. Using this optimized model, we extracted approximately 130 km² of suitable rooftop area, which could support an estimated 18.18 GW of PV capacity. These results demonstrate the effectiveness of deep learning for city-scale rooftop mapping and provide a data-driven basis for strategic planning of distributed PV installations to support carbon neutrality goals. The proposed framework can be generalized to facilitate large-scale solar energy assessments in other cities. Full article

With rising energy costs and the need for sustainable power solutions in urban South African settings, grid-tied renewable energy systems have become viable alternatives for reducing dependence on traditional grid supply. This study investigates the techno-economic feasibility of a grid-connected hybrid photovoltaic (PV) and battery storage system designed for a commercial facility located in Johannesburg, South Africa—an area characterized by a subtropical highland climate. We conducted the analysis using the HOMER Grid software and evaluated the performance of the proposed PV/battery system against the baseline grid-only configuration. Simulation results indicate that the optimal systems, comprising 337 kW of flat-plate PV and 901 kWh of lithium-ion battery storage, offers a significant reduction in electricity expenditure, lowering the annual utility cost from $39,229 to $897. The system demonstrates a simple payback period of less than two years and achieves a net present value (NPV) of approximately $449,491 over a 25-year project lifespan. In addition to delivering substantial cost savings, the proposed configuration also enhances energy resilience. Sensitivity analyses were conducted to assess the impact of variables such as inflation rate, discount rate, and load profile fluctuations on system performance and economic returns. The results affirm the suitability of hybrid grid-tied PV/battery systems for cost-effective, sustainable urban energy solutions in climates with high solar potential. Full article

Understanding temperature variations within the complex urban canopy layer (UCL) is challenging due to limitations and discrepancies between temperature measurements taken in urban canyons and on rooftops. The key question is how much these measurements differ and what factors contribute to these differences. According to the guidance by the World Meteorological Organization (WMO), rooftop observations are not encouraged for urban monitoring, due to potentially anomalous microclimatic conditions, whereas measurements within urban canyons are recommended. This is particularly relevant given the increasing number of rooftop sensors deployed through citizen science, raising questions about the representativeness of such data. This study aimed to address this knowledge gap by comparing temperatures within the UCL using two sensors: one located on a rooftop, and the other positioned within the canyon. The temperature difference between these two nearby locations followed a clear diurnal cycle, peaking at over 1 °C between 12:00 and 16:00 local time, with the canyon warmer than the rooftop. This daytime warming was primarily driven by solar radiation and, to a lesser extent, by wind speed, but only under clear-sky conditions. During the rest of the day, the temperature difference remained negligible. Full article

With rapid global urbanization, glass curtain wall buildings have been widely adopted due to aesthetics and natural lighting. However, during summer time, intense solar radiation leads to significant indoor heat gain, which adversely affect thermal comfort and energy efficiency. The conventional air conditioning systems are typically equipped with a cooling capacity sufficient to maintain an indoor air temperature at the design values specified in the Design standard for energy efficiency of public buildings, which fails to account for the effects of radiation temperature, potentially resulting in reduced thermal comfort and energy inefficiency. By integrating the Thermal Comfort Tool to calculate the PMV index, this study evaluates the affection of solar heat gain on indoor occupants’ thermal comfort and proposes an optimized air temperature control strategy to realize thermal comfort. Based on the dynamic air temperature strategy, an energy consumption model is developed to evaluate the affection of solar radiation on energy consumption for glass curtain wall buildings based on the PMV index. The synergistic effects of shading measures are then evaluated. This study conducts simulation analysis using an office building with a glass curtain wall located in Beijing as a case study. When accounting for radiant heat gain, a significant portion of the time (53.89%) fall outside the thermal comfort range, even when the air conditioning is set to the designated temperature. To maintain thermal comfort, the air conditioning temperature must be lowered by 1.4–3.5 °C, resulting in a 28.08% increase in energy consumption. To address this issue, this study finds that installing interior shading can reduce radiant heat gain. Under the same thermal comfort conditions, the required air temperature reduction is only 0.8–2.1 °C, leading to a 24.26% reduction in energy consumption compared to the case without interior shading. Full article

With the rapid development of science and technology, the global demand for renewable energy is increasing. In the urban context, solar energy has become one of the key ways to increase urban energy self-sufficiency and reduce carbon emissions due to its flexibility in installation and ease of expansion of applications. Therefore, based on Geographic Information System (GIS) and deep learning modeling, this paper proposes a method to efficiently assess the potential of urban rooftop solar photovoltaic (PV), which is analyzed in a typical area of Lanzhou New District, which is divided into 8774 units with an area of 87.74 km². The results show that the method has a high accuracy for the identification of the roof area, with a maximum maxF_β of 0.889. The annual solar PV potential of industrial and residential buildings reached 293.602 GWh and 223.198 GWh, respectively, by using the PV panel simulation filling method for the calculation of the area of roofs where the PV panels can be installed. Furthermore, the rooftop PV potential of the industrial buildings in the research area provided can cover 75.17% of the industrial electricity consumption. This approach can provide scientific guidance and data support for regional solar PV planning, which should prioritize the development of solar potential of industrial buildings in the actual consideration of rooftop PV deployment planning. Full article

The transition to renewable energy is critical to enhance Puerto Rico’s energy resilience and reduce dependence on imported fossil fuels. Rooftop photovoltaic (PV) systems provide a scalable opportunity to meet these objectives. This study evaluates the potential of rooftop PV systems across Puerto Rico using the National Renewable Energy Laboratory’s (NREL) PV Rooftop Database, processing detailed roof surface data to estimate installed capacity, energy generation, Levelized Cost of Electricity (LCOE), and solar resource potential at municipal and barrio levels. Findings reveal high solar rooftop capacity in urban neighborhoods, with areas like Sabana Abajo and Hato Tejas each exceeding 450 GWh/year in potential generation. Solar rooftop resource values peak at 3.67 kWh/kW in coastal areas, with LCOE values (0.071–0.215 USD/kWh) below current electricity rates. All municipalities demonstrate technical potential to meet their electricity demand with rooftop PV system alone. This research contributes through (1) developing Puerto Rico’s first comprehensive solar rooftop potential map; (2) providing unprecedented barrio-level analysis; (3) introducing a methodology for estimating missing post-disaster consumption data; and (4) integrating technical, economic, and equity indicators to inform energy policy. These findings demonstrate the importance of rooftop solar in achieving renewable energy goals and provide an understanding of spatial energy inequalities. Full article

Urban thermal environment is directly linked to the health and comfort of local residents, as well as energy consumption. Drone-based thermal infrared image acquirement provides an efficient and flexible way of assessing urban heat distribution, thereby supporting climate-resilient and sustainable urban development. Here, we present an advanced approach that utilizes the thermal infrared camera mounted on the drone for high-resolution building wall temperature measurement and achieves centimeter accuracy. According to the binocular vision theory, the three-dimensional (3D) reconstruction of thermal infrared images is first conducted, and then the two-dimensional building wall temperature is extracted. Real-world validation shows that our approach can measure the wall temperature within a 5 °C error, which confirms the reliability of this approach. The field measurement of Yuquanting in Xiong’an New Area China during three time periods, i.e., morning (7:00–8:00), noon (13:00–14:00) and evening (18:00–19:00), was used as a case study to demonstrate our approach. The results show that during the heating season, the building wall temperature was the highest at noon time and the lowest in evening time, which were mostly caused by solar radiation. The highest wall temperature at noon time was 55 °C, which was under direct sun radiation. The maximum wall temperature differences were 39 °C, 55 °C, and 20 °C during morning, noon and evening time, respectively. The lighter wall coating color tended to have a lower temperature than the darker wall coating color. Beyond this application, this approach has potential in future autonomous thermal environment measuring systems as a foundational element. Full article

Urban thermal environment challenges in China have made outdoor thermal comfort a key factor in evaluating spatial quality and livability. Building layout not only affects internal performance but also shapes the microclimate of surrounding outdoor spaces. The climatic characteristics of temperate monsoon climate regions significantly impact residents’ outdoor activities. Most existing studies focus solely on either the external thermal environment or the buildings themselves in isolation. This study focuses on Beijing, a representative city in the temperate monsoon climate zone, and explores block-scale spatial optimization using computational typology. The objective is to balance architectural performance with outdoor thermal comfort in both winter and summer. Optimization targets include the Universal Thermal Climate Index (UTCI), winter sunshine duration, and summer solar radiation. Results show winter UTCI can be optimized to −6.13 °C to −1.18 °C and summer UTCI to 28.19 °C to 29.17 °C, with greater optimization potential in winter (23.5% higher). Synergistic relationships are observed between winter comfort and sunshine duration (coefficient: 0.777) and between summer comfort and solar radiation (coefficient: 0.947). However, trade-offs exist between seasonal comfort indicators, with strong conflicts between winter and summer objectives. Two distinct form types—“low-south-high-north enclosed” for winter and “high-rise point-type low-density” for summer—are identified as effective for seasonal adaptation. The study proposes an integrated method combining data-driven generation, multi-objective optimization, and clustering-based decision-making. This approach moves beyond traditional empirical design, offering a quantitative and adaptable strategy for climate-responsive urban block planning and supporting low-carbon urban transformation. Full article