Search Results (88)

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

Solar photovoltaic technology has become a cornerstone of the renewable energy sector over the last 20 years, yet its efficiency remains sensitive to environmental and operational conditions. This study rigorously analyzes how irradiance, temperature, humidity, wind speed, and soiling affect key electrical outputs—Direct current, power, and voltage—of solar panels using advanced robust regression methods: Ridge Regression, Least Absolute Deviation, and M-Estimation. Our results demonstrate that irradiance is the dominant driver of performance, with Ridge Regression coefficients reaching up to 1193 for power. The M-estimation model achieved high predictive accuracy, with R² Scores of 0.989 for current (Mean Squared Error = 0.0399) and 0.991 for power (Mean Squared Error ≈ 10,445), indicating strong model reliability. voltage prediction was more challenging but still robust (R² = 0.836, Mean Squared Error = 49.63). Negative impacts from ambient temperature and humidity were consistently observed across models, while wind speed exhibited a beneficial effect by enhancing cooling and thus improving current and power outputs. Soiling was also identified as a critical factor, significantly reducing power and voltage generation. These findings provide quantifiable evidence of how environmental variables shape solar photovoltaic performance and underscore the importance of environmental monitoring and maintenance strategies to optimize energy yield in operational solar power systems. Full article

This study investigates how various renewable energy technologies influence national carbon intensity (CO₂ emissions per unit of GDP) across 184 countries over the period 2000–2020. In the context of Sustainable Development Goals (SDG 7 and SDG 13) and the post-Paris-Agreement policy landscape, it addresses the gap in understanding technology-specific decarbonization effects and the role of governance. A dynamic panel framework employing the Dynamic Common Correlated Effects (DCCE) estimator accounts for cross-sectional dependence and temporal persistence, while disaggregating total renewables into hydropower, wind, solar, and geothermal generation. Environmental regulation is incorporated as a moderating variable using the World Bank’s Regulatory Quality index. Empirical results demonstrate that higher renewable generation is associated with statistically significant reductions in carbon intensity, with hydropower showing the most consistent negative effect across all income groups. Solar and geothermal technologies yield substantial carbon-reducing impacts in lower-middle-income settings once supportive policies are in place. Wind exhibits heterogeneous outcomes: positive or insignificant effects in some high- and upper-middle-income panels prior to 2015, shifting toward neutral or negative after more stringent regulation. Interaction terms reveal that stronger regulatory environments amplify renewable-driven decarbonization, particularly for intermittent sources such as wind and solar. Key contributions include (1) a comprehensive global assessment of four disaggregated renewable technologies; (2) integration of regulatory quality into decarbonization pathways, illustrating post-2015 policy moderations; and (3) methodological advancement through a large-sample DCCE approach that captures unobserved common shocks and heterogeneous country dynamics. These findings inform targeted policy measures—such as prioritizing hydropower where feasible, strengthening regulatory frameworks, and tailoring technology strategies—to accelerate low-carbon energy transitions worldwide. Full article

For energy management systems, it is crucial to determine, in advance, the available energy from renewable sources to be dispatched in the next hours or days, in order to meet their generation and consumption goals. Predicting the photovoltaic power output strongly depends on accurate weather forecasting data and properly photovoltaic panel models. In this context, several traditional thermal models are expected to become obsolete due to the removal of the widely used Nominal Module Operating Temperature parameter, stated in the IEC 61215-2:2021 standard, according to reports of longer time periods in test data processing. The main contribution of the photovoltaic power estimation algorithm developed in this paper is the integration of an accurate procedure to calculate the hourly day-ahead power output of a photovoltaic plant, based on three simplified thermal models in steady state. These models are proposed and evaluated as remedial alternatives to the removal of the Nominal Module Operating Temperature parameter—a subject that has not been widely addressed in the related literature. The proposed estimation algorithm converts specific Numerical Weather Prediction data and solar module specifications into photovoltaic power output, which can be used in energy management applications to provide economic and ecological benefits. This approach focuses on rooftop-mounted mono-crystalline silicon photovoltaic panel arrays and incorporates a nonlinear translation of Standard Test Conditions parameters to real operating conditions. All necessary input data are provided for the analysis, and the accuracy of experimental results is validated using appropriate error metrics. Full article

The global need to transition towards sustainable energy sources has increased the exploration of efficient methods to harness solar energy. Traditional solar panels, being stationary, often fail to capture the rays of the sun optimally across the day. This paper presents a SunPath navigator system that dynamically adjusts the solar panel’s angle, ensuring maximum exposure to the sun. The developed SunPath navigator system achieves a 27.67% average energy gain. This work has utilised the applications of various machine learning models, such as decision trees, AdaBoost, and K-nearest neighbour, for predicting energy generation. The relevance of these models is analysed based on multiple types of error such as MAE, MSE, RMSE, and R². The decision tree outperforms the other two models with a minimum error rate. It is paving the way for a future where solar energy is a primary, economical, and user-friendly power source in urban and rural areas. The dual-axis tracking system not only enhances energy generation but also estimates future energy generation. Full article

The growing demand for renewable energy positions it as a cornerstone for climate change mitigation and greenhouse gas emissions reduction. Although renewable energy sources generate around 30% of global electricity, their production and deployment involve significant environmental challenges. This review analyzes renewable energy projects from a life cycle perspective, focusing on environmental impacts throughout the supply chain. Particular emphasis is placed on the energy-intensive nature of manufacturing phases, which account for 60% to 80% of total emissions. The extraction of critical raw materials such as neodymium, dysprosium, indium, tellurium, and silicon is associated with emission levels ranging from 0.02 to 0.09 kg of carbon dioxide equivalent per kilowatt-hour for rare earth elements, along with an estimated average land degradation of 0.2 hectares per megawatt installed. Furthermore, the production of solar-grade silicon for photovoltaic panels consumes approximately 293 kilowatt-hours of electricity per kilogram, significantly contributing to the overall environmental footprint. Through a comprehensive review of the existing literature, this study integrates life cycle assessment and sustainable supply chain management approaches to identify environmental hotspots, quantify emissions, and propose strategic improvements. The analysis provides a structured, systematized, and data-driven evaluation, highlighting the relevance of circular economy principles, advanced recycling technologies, and digital innovations to enhance sustainability, traceability, and resilience in renewable energy supply chains. This work offers actionable insights for decision-makers and policymakers to guide the low-carbon transition. Full article

Renewable energy technologies have become an increasingly important component of the global energy supply. In recent years, photovoltaic and wind energy have been the fastest-growing renewable sources. Although oceans present harsh environments, their estimated energy generation potential is among the highest. Ocean-based solutions are gaining significant momentum, driven by the advancement of offshore wind, floating solar, tidal, and wave energy, among others. The integration of various marine energy sources with green hydrogen production can facilitate the exploitation and transportation of renewable energy. This paper presents a mathematics-driven analysis for the simulation of a technical model designed as a generic framework applicable to any location worldwide and developed to analyze the integration of solar energy generation and green hydrogen production. It evaluates the impact of key factors such as solar irradiance, atmospheric conditions, water surface flatness, as well as the parameters of photovoltaic panels, electrolyzers, and adiabatic compressors, on both energy generation and hydrogen production capacity. The proposed mathematics-based framework serves as an innovative tool for conducting multivariable parametric analyses, selecting optimal design configurations based on specific solar energy and/or hydrogen production requirements, and performing a range of additional assessments including, but not limited to, risk evaluations, cause–effect analyses, and/or degradation studies. Enhancing the efficiency of solar energy generation and hydrogen production processes can reduce the required photovoltaic surface area, thereby simplifying structural and anchoring requirements and lowering associated costs. Simpler, more reliable, and cost-effective designs will foster the expansion of floating solar energy and green hydrogen production in marine environments. Full article

This research evaluates how wind and solar PV systems balance together. Increasing the share of stochastic renewable energy production in electricity and hot turning reserve deficit are welcome compensation issues. This research used weather station data from an open seashore from the last 10 years, 2014–2023, on the Estonian island Saaremaa’s west coast to evaluate yearly fluctuations. We used the indicator demand cover factor to estimate the coincidence of wind generation and solar PV system electricity. For clarity, the initial data were prepared by assuming the equality of production and consumption annual data by scaling the obtained data. This study demonstrates that the best compensating possibilities are the share of wind generation and solar PV electricity mix, respectively, equal to 0.7/0.3 and 0.8/0.2, reaching a demand cover factor of 0.62. This study evaluated the demand cover factor’s dependence on increased production compared to consumption. This study used different batteries to research the influence of these demand cover factors. Furthermore, this research makes a significant contribution by showcasing how to turn weather station data into real wind generator and PV panel production data. Full article

The aim of this study is to evaluate the environmental impact of solar energy by analyzing its emissions, resource consumption, and waste generation throughout its life cycle. As one of the most widely adopted energy sources, solar power offers substantial benefits in reducing greenhouse gas emissions; however, its broader environmental footprint requires careful examination. The production, operation, and disposal of solar panels contribute to pollution, water consumption, and hazardous waste accumulation, with an estimated 250,000 tons of solar waste reported in 2016 alone. Furthermore, solar power generation requires significant water resources, averaging 650 gallons per megawatt-hour of electricity. A key focus of this study is the emissions associated with solar technology, particularly during panel manufacturing and operation. Using HOMER Pro software, this research quantifies the emissions from Trina Solar photovoltaic (PV) panels (345 Wp), revealing an annual output of 49,259 kg of carbon dioxide, 214 kg of sulfur dioxide, and 104 kg of nitrogen dioxide. This Study obtained using HOMER Pro primarily account for operational emissions and do not include full lifecycle impacts such as raw material extraction, transportation, and disposal. These findings highlight the trade-offs between solar energy’s environmental advantages and its indirect ecological costs. While solar systems contribute to energy security and long-term economic savings, their environmental implications must be factored into energy planning and sustainability strategies. This study underscores the importance of developing greener manufacturing processes, improving recycling strategies, and optimizing solar farm operations to reduce emissions and resource depletion. By providing a comprehensive assessment of solar energy’s environmental impact, this research contributes valuable insights for policymakers, researchers, and industry leaders seeking to balance the benefits of solar power with sustainable environmental management. Full article

This study introduces an adaptive robust approach for optimally sizing hybrid renewable energy systems (HRESs) comprising solar panels, wind turbines, batteries, and a diesel generator. It integrates vector auto-regressive models (VAR) and neural networks (NN) into dynamic uncertainty sets (DUSs) to address temporal auto-correlations and cross-correlations among uncertain parameters like energy demand and solar and wind energy supply. These DUSs are compared to static and independent dynamic uncertainty sets based on time series (TS) from the literature. An exact iterative algorithm is developed to solve the problem effectively. A case study of a northern Ontario community evaluates the proposed framework and the solution method using real test data. Simulation reveals a 10.7% increase in capital cost on average but a 36.2% decrease in operational cost, resulting in a 16.4% total cost reduction and an 8.1% improvement in system reliability compared to the nominal model employing point estimates. Furthermore, the proposed VAR- and NN-based DUSs significantly outperform classical static and TS-based dynamic sets, underscoring the necessity of considering cross-correlations in uncertainty quantification. Full article

Numerous studies have explored the placement of solar panels on the facades or roofs of buildings. This study investigates a new approach to estimating energy generation from transparent, double-sided solar panels integrated into the facade of an existing building, focusing on how the façade’s color influences panel performance. The most significant advantages of integrating double-sided and transparent solar panels on the sides of a building are the natural lighting provided by the sunlight entering the building and the additional energy generated when the radiation returns to the back of the panel. The light beam strikes the front panel, allowing some radiation to pass through the transparent panel to the back side, where it hits the surface. Part of the beam is then reflected toward the rear panel. The fraction of light reflected (albedo) depends on the surface’s color. We first constructed a double-sided, transparent solar panel and integrated it with MATLAB software 2024 code. The model was verified by comparing the simulation results, specifically the I–V and P–V graphs, with data from the manufacturer’s specifications. We conducted an extensive investigation into panels installed on surfaces made of different materials during each installation. This investigation aimed to understand the behavior and performance of the panels when installed on the surfaces of various materials. Full article

This research investigates the feasibility of utilizing anaerobic digestion to produce biogas from organic waste generated at an equestrian center, emphasizing energy savings and environmental sustainability. The biogas system produces an estimated 85,495 kWh annually, surpassing the center’s electricity consumption of 18,644 kWh. This reduces greenhouse gas emissions by 2753 kg of CO₂. Photovoltaic systems, which meet 70.77% of the energy demand, further contribute to a reduction of 1178 kg of CO₂. Substituting fossil fuels with biofuels and planting 1700 trees achieved reductions of 26,263 kg of CO₂ and 51,033 kg of CO₂, respectively, resulting in a 49% overall carbon footprint reduction. This study evaluates the economic viability of biogas systems in the equestrian sector and optimal feedstock characteristics for efficient production. Additionally, complementary strategies, including photovoltaic solar panels and water management systems, are analyzed for their roles in promoting sustainable resource management. These integrated solutions support a transition to a circular economy while reducing environmental impacts and fostering energy independence in the equestrian industry. Full article

The integration of biogas and photovoltaic solar energy systems in sanitary landfills represents a promising strategy for sustainable energy generation and efficient urban waste management. This study evaluates the potential for biogas and photovoltaic energy production in two cells of the Municipal Landfill of Chihuahua, Mexico. Using the LandGEM and MMB models (Landfill Gas Emission Model and the Mexican Biogas Model), biogas generation was estimated by considering the composition of the landfill gas and the characteristics of the cover in each cell, revealing notable differences due to their operational periods and waste deposition. Photovoltaic simulations, conducted with the HelioScope software 2020, evaluated spatial configurations and solar radiation data. The generation potential for 2025 was simulated using predictive models, yielding results between 25.48 and 26.08 MW for the biogas–photovoltaic system, depending on the orientation of the panels and the optimization of the coverage. The novelty of this work lies in the combined evaluation of biogas and photovoltaic potential within a single landfill site, integrating advanced modeling tools to optimize system design. By demonstrating the feasibility and benefits of this hybrid system, the study contributes to clean energy solutions, environmental mitigation, and improved waste management strategies. Our findings emphasize the importance of site-specific management practices and predictive modeling to enhance renewable energy production and reduce greenhouse gas emissions, supporting sustainable urban development initiatives. Full article

The adoption of photovoltaic solar technology for renewable energy generation has been growing rapidly worldwide. In decarbonization processes, the use of photovoltaic panels has been preferred due to their reliability, safety, and efficiency. Specifically, the use of photovoltaic panels has increased significantly in Chile, as the climatic conditions are ideal for photovoltaic solar technology. The expected lifespan of a photovoltaic panel is approximately 25 years, so the amount of photovoltaic waste is projected to rise significantly in the coming decades. Consequently, interest has emerged in establishing policies and processes for recycling and recovering value from photovoltaic waste. The objective of this study is to develop a life cycle assessment (LCA) of the leaching process of photovoltaic modules using nitric acid as a leaching agent and to employ the results to analyze the projected scenario for the Antofagasta region in 2040. Through statistical analysis of currently approved photovoltaic installations, projections were made to estimate the amount of photovoltaic waste and the total value of recyclable material expected to be available in 2040, resulting in an approximate figure of 30,676,367 discarded modules. Simultaneously, a life cycle assessment of the leaching process for photovoltaic waste using nitric acid was conducted using the OpenLCA software. The analysis showed that the proposed process has a high impact on global warming potential (GWP), generating 7.07 kg of CO₂ equivalent per kilogram of photovoltaic cell waste. Finally, an environmental and economic comparative analysis was performed, comparing nitric acid with ionic liquids previously studied by the research group. Preliminary results concluded that nitric acid has a significantly lower environmental impact and production cost. Full article

As the world increasingly embraces renewable energy as a sustainable power source, accurately assessing of solar energy potential becomes paramount. Photovoltaic (PV) systems, especially those integrated into urban rooftops, offer a promising solution to address the challenges posed by aging energy grids and rising fossil fuel prices. However, optimizing the placement of PV panels on rooftops remains a complex task due to factors like building shape, location, and the surrounding environment. This study introduces the Roof-Solar-Max methodology, which aims to maximize the placement of PV panels on urban rooftops while avoiding shading and panel overlap. Leveraging geographic information systems technology and 3D models, this methodology provides precise estimates of PV generation potential. Key contributions of this research include a roof categorization model, identification of PV-ready rooftops, optimal spatial distribution of PV panels, and innovative evaluation technology. Practical implementation in a real urban setting demonstrates the methodology’s utility for decision making in the planning and development of solar energy systems in urban areas. The main findings highlight substantial potential for PV energy generation in the studied urban area, with capacities reaching up to 444.44 kW. Furthermore, implementing PV systems on residential rooftops has proven to be an effective strategy for reducing CO₂ emissions and addressing climate change, contributing to a cleaner and more sustainable energy mix in urban environments. Full article