Search Results (1,413)

Integrating renewable energy with biomass valorization offers a scalable pathway toward circular and climate-resilient campus operations. This study presents a replicable model implemented at Alanya Alaaddin Keykubat University (ALKU, Türkiye), where post-consumer food waste from 30 cafeteria menus is converted into pet food and compost using a 150 L ECOAIR-150 thermal drying and grinding unit powered entirely by a 1.7 MW rooftop photovoltaic (PV) system. The PV infrastructure, established under Türkiye’s first public-sector Energy Performance Contract (EPC), ensures zero-electricity-cost operation. On average, 260 kg of organic waste are processed monthly, yielding 180 kg of pet food and 50 kg of compost, with an energy demand of 1.6 kWh h⁻¹ and a conversion efficiency of 68.4%, resulting in approximately 17.5 t CO₂ emissions avoided annually. Economic analysis indicates a monthly revenue of USD 55–65 and a payback period of ~36 months. Sensitivity analysis highlights the influence of input quality, seasonal waste composition, PV output variability, and operational continuity during academic breaks. Compared with similar initiatives in the literature, this model uniquely integrates EPC financing, renewable energy generation, and waste-to-product transformation within an academic setting, contributing directly to SDGs 7, 12, and 13. Full article

Stains, defects, and snow accumulation constitute three prevalent photovoltaic (PV) anomalies; each exhibits unique color and thermal signatures yet collectively curtail energy yield. Existing detectors typically sacrifice accuracy for speed, and none simultaneously classify all three fault types. To counter the identified limitations, an enhanced YOLOv11 framework is introduced. First, the hue-saturation-value (HSV) color model is employed to decouple hue and brightness, strengthening color feature extraction and cross-sensor generalization. Second, an outlook attention module integrated into the backbone precisely delineates micro-defect boundaries. Third, a mix structure block in the detection head encodes global context and fine-grained details to boost small object recognition. Additionally, the bounded sigmoid linear unit (B-SiLU) activation function optimizes gradient flow and feature discrimination through an improved nonlinear mapping, while the gradient-weighted class activation mapping (Grad-CAM) visualizations confirm selective attention to fault regions. Experimental results show that overall mean average precision (mAP) rises by 1.8%, with defect, stain, and snow accuracies improving by 2.2%, 3.3%, and 0.8%, respectively, offering a reliable solution for intelligent PV inspection and early fault detection. Full article

This study investigates the electro-thermal transient response of a photovoltaic–thermal (PV/T)–heat pump system under dynamic disturbances to optimize operational stability. A dynamic model integrating a PV/T collector and a heat pump was developed by the transient heat current method, enabling high-fidelity simulations of step perturbations: solar irradiance reduction, compressor operation, condenser water flow rate variations, and thermal storage tank volume changes. This study highlights the thermal storage tank’s critical role. For V_tank = 2 m³, water tank volume significantly suppresses the water tank and PV/T collector temperature fluctuations caused by solar irradiance reduction. PV/T collector temperature fluctuation suppression improved by 46.7%. For the PV/T heat pump system in this study, the water tank volume was selected between 1 and 1.5 m³ to optimize the balance of thermal inertia and cost. Despite PV cell electrical efficiency gains from PV cell temperature reductions caused by solar irradiance reduction, power recovery remains limited. Compressor dynamic performance exhibits asymmetry: the hot water temperature drop caused by speed reduction exceeds the rise from speed increase. Load fluctuations reveal heightened risk: load reduction triggers a hot water 7.6 °C decline versus a 2.2 °C gain under equivalent load increases. Meanwhile, water flow rate variation in condenser identifies electro-thermal time lags (100 s thermal and 50 s electrical stabilization), necessitating predictive compressor control to prevent temperature and compressor operation oscillations caused by system condition changes. These findings advance hybrid renewable systems by resolving transient coupling mechanisms and enhancing operational resilience, offering actionable strategies for PV/T–heat pump deployment in building energy applications. Full article

To reduce the carbon emissions of existing residential buildings while pursuing maximum cost benefits, a multi-optimization design method for the existing residential building rooftops, retrofitted by attaching the solar photovoltaic panels and thermal collectors, was proposed in the study. At first, the life cycle carbon emission and cost benefit of the retrofitted buildings were assigned as the optimization objectives, and the models of carbon emission and cost benefit were developed. Furthermore, a typical existing residential community located in the cold zone of China was selected to perform the multi-optimization based on the Grasshopper platform. Meanwhile, the laying area, laying angle, and allocation ratio of the solar photovoltaic panels and thermal collectors were selected as the design parameters. And then the best retrofitting solution suitable for the existing residential buildings was proposed. The results show that the weightings of the carbon emission of retrofitting life cycle are 42.68%, and that for the cost benefit is 57.32%. Significantly, there is a 31% reduction in carbon emissions compared to the building before retrofitting, and a 24.7% reduction in cost benefit. Full article

As energy systems transition toward high shares of variable renewable generation, local energy communities (ECs) are increasingly relevant for enabling demand-side flexibility and self-sufficiency. This shift is particularly evident in the residential sector, where the deployment of photovoltaic (PV) systems is rapidly growing. While mixed-integer linear programming (MILP) remains the standard for operational optimization and demand response in such systems, its computational burden limits scalability and responsiveness under real-time or uncertain conditions. Reinforcement learning (RL), by contrast, offers a model-free, adaptive alternative. However, its application to real-world energy system operation remains limited. This study explores the application of a Deep Q-Network (DQN) to a real residential EC, which has received limited attention in prior work. The system comprises three single-family homes sharing a centralized heating system with a thermal energy storage (TES), a PV installation, and a grid connection. We compare the performance of MILP and RL controllers across economic and environmental metrics. Relative to a reference scenario without TES, MILP and RL reduce energy costs by 10.06% and 8.78%, respectively, and both approaches yield lower total energy consumption and CO₂-equivalent emissions. Notably, the trained RL agent achieves a near-optimal outcome while requiring only 22% of the MILP’s computation time. These results demonstrate that DQNs can offer a computationally efficient and practically viable alternative to MILP for real-time control in residential energy systems. Full article

This study evaluates the energy performance and efficiency of a low-concentration photovoltaic (CPV) system integrated with a phase change material (PCM), referred to as the CPV–PCM system, which stores and delivers thermal energy for building applications. A paraffin-based PCM with a melting point range of 58–60 °C was selected to align with typical building temperature requirements. The system was tested over three consecutive days in July at Al Ain, United Arab Emirates, under extreme climatic conditions (2100 W/m² solar irradiance, 35–45 °C ambient temperature), and its performance was compared to standard CPV and traditional tracked PV systems. The results demonstrate that PCM integration significantly enhances thermal regulation, reducing CPV peak temperatures by 38 °C (from 123 °C to 85 °C) and average temperatures by 22 °C (from 88 °C to 66 °C). The CPV–PCM system achieved a total energy efficiency of 60%, doubling that of standard CPV (30%) and tracked PV (25%), with cumulative electrical and thermal energy outputs of 370 Wh and 290 Wh, respectively. This dual electrical–thermal output enables the system to meet building heating demands, such as ~200–300 Wh/m² for domestic hot water and ~100–150 Wh/m² for space heating in United Arab Emirates winters, positioning it as a sustainable solution for energy-efficient buildings in arid regions. The findings underscore the advantages of PCM-based thermal control in CPV systems for hot climates, addressing gaps in prior studies focused on moderate conditions. Future research should explore long-term durability, optimized containment techniques, and alternative PCMs to further improve performance. Full article

This study offers a detailed environmental, energy, and economic evaluation of thermal modernisation options for an existing single-family home in southern Poland. A total of 24 variants, combining different heat sources (solid fuel, biomass, natural gas, and heat pumps) with various levels of building insulation, were analysed using energy performance certification methods. Results show that, from an energy perspective, the most advantageous scenarios are those utilising brine-to-water or air-to-water heat pumps supported by photovoltaic systems, reaching final energy demands as low as 43.5 kWh/m²year and primary energy demands of 41.1 kWh/m²year. Biomass boilers coupled with solar collectors delivered the highest renewable energy share (up to 99.2%); however, they resulted in less notable reductions in primary energy. Environmentally, all heat pump options removed local particulate emissions, with CO₂ reductions of up to 87.5% compared to the baseline; biomass systems attained 100% CO₂ reduction owing to renewable fuels. Economically, biomass boilers had the lowest unit energy production costs, while PV-assisted heat pumps faced the highest overall costs despite their superior environmental benefits. The findings highlight the trade-offs between ecological advantages, energy efficiency, and investment costs, offering a decision-making framework for the modernisation of sustainable residential heating systems. Full article

Renewable energy sources, particularly solar photovoltaics (PVs), are rapidly expanding to achieve carbon neutrality. Integrated photovoltaic (IPV) solutions in underutilized spaces offer a viable option for countries with land constraints and public opposition. Vertical PV (VPV) systems, featuring bifacial solar modules installed vertically, facing east and west, present a promising alternative. In contrast to conventional tilted PV (CPV) systems, which peak around midday, VPV systems generate more power in the morning and afternoon. This mitigates issues such as the duck curve and curtailment caused by midday overgeneration. Moreover, combining VPV and CPV systems can increase the solar hosting capacity of a distribution line (DL) for PV-system interconnections, driving research interest. This study assessed the hosting-capacity improvements from VPV systems by analyzing voltage fluctuations and thermal constraints using OpenDSS software (Version 9.1.1.1). The potential substitution effect of a smart inverter-based energy-storage system (ESS) was also explored. The analysis, based on real-grid conditions in South Korea, incorporated actual DL data, generation and demand profiles, and operational data from both VPV and CPV systems. Worst-case scenarios were simulated to evaluate their impact on grid stability. The results demonstrate that VPV systems can increase hosting capacity by up to 23% and ensure stable grid operation by reducing power-generation uncertainties. Full article

The lack of cultural spaces and the inadequate preservation of architectural heritage hinder the development of ecotourism in Ollantaytambo. This research aims to propose an architectural design for green infrastructure that supports the growth of ecotourism at the Ollantaytambo archeological site, located in the Urubamba Province, Peru. The study consists of three main phases: a literature review; a site analysis focusing on climate, flora, and fauna; and the development of a comprehensive architectural proposal. The process is supported by digital tools, including Google Earth Pro 2024, OpenStreetMap 2024, SketchUp 2024, Lumion 2024, Photoshop 2024, and 3D Sun-Path 2024. The resulting design includes the implementation of a sustainable cultural center, conceived to ensure seasonal thermal comfort through the use of green roofs and walls, efficient irrigation systems, and native vegetation. The proposal incorporates elements of Cusco’s vernacular architecture by combining traditional earth-based construction techniques, such as rammed earth, adobe, and quincha, with contemporary materials, such as bamboo and timber, in order to improve the energy and environmental performance of the built environment. Furthermore, the project integrates a rainwater-harvesting system and a photovoltaic lighting system. It includes 30 solar-powered luminaires with an estimated monthly output of 72 kWh, and 135 photovoltaic panels capable of generating approximately 2673 kWh per month. In conclusion, the proposed design blends naturally with the local environment and culture. It adheres to principles of sustainability and energy efficiency and aligns with Sustainable Development Goals (SDGs) 3, 6, 7, 11, and 15 by promoting heritage conservation, environmental regeneration, and responsible ecotourism. Full article

Photovoltaic (PV) modules undergo comprehensive testing to validate their electrical and thermal properties prior to market entry. These evaluations consist of durability and efficiency tests performed under realistic outdoor conditions with natural climatic influences, as well as in controlled laboratory settings. The overall performance of PV cells is affected by several factors, including solar irradiance, operating temperature, installation site parameters, prevailing weather, and shading effects. In the presented study, three distinct PV modules were analyzed using a sophisticated large-scale steady-state solar simulator. The current–voltage (I-V) characteristics of each module were precisely measured and subsequently scrutinized. To augment the analysis, a three-layer artificial neural network, specifically the multilayer perceptron (MLP), was developed. The experimental measurements, along with the outputs derived from the MLP model, served as the foundation for a comprehensive global sensitivity analysis (GSA). The experimental results revealed variances between the manufacturer’s declared values and those recorded during testing. The first module achieved a maximum power point that exceeded the manufacturer’s specification. Conversely, the second and third modules delivered power values corresponding to only 85–87% and 95–98% of their stated capacities, respectively. The global sensitivity analysis further indicated that while certain parameters, such as efficiency and the ratio of V_oc/V, played a dominant role in influencing the power-voltage relationship, another parameter, U, exhibited a comparatively minor effect. These results highlight the significant potential of integrating machine learning techniques into the performance evaluation and predictive analysis of photovoltaic modules. Full article

Accurate photovoltaic (PV) power forecasting is essential for grid integration, particularly in maritime climates with dynamic weather patterns. This study addresses high-dimensional meteorological data challenges by systematically evaluating 32 variables across four categories (solar irradiance, temperature, atmospheric, hydrometeorological) for day-ahead PV forecasting using long short-term memory (LSTM) networks. Using six years of data from a 350 kWp solar farm in Scotland, we compare satellite-derived data and local weather station measurements. Surprisingly, downward thermal infrared flux—capturing persistent atmospheric moisture and cloud properties in maritime climates—emerged as the most influential predictor despite low correlation (1.93%). When paired with precipitation data, this two-variable combination achieved 99.81% R², outperforming complex multi-variable models. Satellite data consistently surpassed ground measurements, with 9 of the top 10 predictors being satellite derived. Our approach reduces model complexity while improving forecasting accuracy, providing practical solutions for energy systems. Full article

This study presents a novel switchable multi-inlet Building integrated photovoltaic/thermal (BIPV/T) curtain wall system designed to enhance solar energy utilization in commercial buildings. The system integrates controllable air inlets and motorized dampers that dynamically adjust airflow patterns in response to real-time environmental conditions such as solar irradiance, ambient air temperature, and PV panel temperature. A steady-state energy balance model, developed using a thermal network analogy and implemented in Python, was used to simulate winter operation in Montréal, Canada. Three operating modes with different air inlet configurations were assessed to evaluate system performance across variable air velocities and solar conditions. Results indicate that the switchable system improves combined thermal and electrical generation by 2% to 25% compared to fixed one- or two-inlet systems. Under low irradiance and air velocity, one-inlet operation is dominant, while higher solar gain and airflow favor two-inlet configurations. The system demonstrates effective temperature control and enhanced energy yield through optimized airflow management. This work highlights the potential of integrated control strategies and modular façade design in improving the efficiency of solar building envelope systems and offers practical implications for scalable deployment in energy-efficient, heating-dominated climates. Full article

This review explores the pressing need for decarbonization strategies in the off-grid Indigenous communities of Northern Quebec, particularly focusing on Nunavik, where reliance on diesel and fossil fuels for heating and electricity has led to disproportionately excessive greenhouse gas emissions. These emissions underscore the urgent need for sustainable energy alternatives. This study investigates the potential for improving building energy efficiency through advanced thermal insulation, airtight construction, and the elimination of thermal bridges. These measures have been tested in practice; for instance, a prototype house in Quaqtaq achieved over a 54% reduction in energy consumption compared to the standard model. Beyond efficiency improvements, this review assesses the feasibility of renewable energy sources such as wood pellets, solar photovoltaics, wind power, geothermal energy, and run-of-river hydropower in reducing fossil fuel dependence in these communities. For instance, the Innavik hydroelectric project in Inukjuak reduced diesel use by 80% and is expected to cut 700,000 t of CO₂ over 40 years. Solar energy, despite seasonal limitations, can complement other systems, particularly during sunnier months, while wind energy projects such as the Raglan Mine turbines save 4.4 million liters of diesel annually and prevent nearly 12,000 t of CO₂ emissions. Geothermal and run-of-river hydropower systems are identified as long-term and effective solutions. This review emphasizes the role of Indigenous knowledge in guiding the energy transition and ensuring that solutions are culturally appropriate for community needs. By identifying both technological and socio-economic barriers, this review offers a foundation for future research and policy development aimed at enabling a sustainable and equitable energy transition in off-grid Northern Quebec communities. Full article

Inorganic halide perovskites have garnered significant attention as promising candidates for photovoltaic and optoelectronic applications, owing to their enhanced thermal and chemical stability relative to hybrid perovskite materials. This review synthesizes recent progress in vapor-phase deposition methodologies, such as co-evaporation, close space sublimation (CSS), continuous flash sublimation (CFS), and chemical vapor deposition (CVD), which enable the precise modulation of film composition and morphology. Advances in material systems, including the stabilization of CsPbI₂Br, the introduction of tin-doped phases, and the investigation of lead-free double perovskites like Cs₂AgSbI₆ and Cs₂AgBiCl₆, are critically evaluated with respect to their impact on device performance. The incorporation of these materials into photovoltaic devices and tandem configurations is explored, with particular emphasis on improvements in power conversion efficiency and operational durability. Furthermore, interface engineering approaches tailored to vacuum-deposited films—such as defect passivation and energy-level alignment—are examined in detail. The potential for scalable manufacturing is assessed through simulation analyses, throughput modeling, and pilot-scale demonstrations, underscoring the feasibility of industrial-scale production. By offering a comprehensive overview of these advancements, this review provides valuable perspectives on the current landscape and prospective trajectories of vapor-deposited inorganic perovskite technologies. Full article

Among the various renewable-powered pathways for green hydrogen production, solar photovoltaic (PV) technology represents a particularly promising option due to its environmental sustainability, widespread availability, and declining costs. However, the inherent intermittency of solar irradiance presents operational challenges for electrolyzers, particularly in terms of stability and efficiency. This study presents a MATLAB-based dynamic model of an off-grid, DC-coupled solar PV-Anion Exchange Membrane (AEM) electrolyzer system, with a specific focus on realistically estimating hydrogen output. The model incorporates thermal energy management strategies, including electrolyte pre-heating during startup, and accounts for performance degradation due to load cycling. The model is designed for a comprehensive analysis of hydrogen production by employing a 10-year time series of irradiance and ambient temperature profiles as inputs. The results are compared with two simplified scenarios: one that does not consider the equipment response time to variable supply and another that assumes a fixed start temperature to evaluate their impact on productivity. Furthermore, to limit the effects of degradation, the algorithm has been modified to allow the non-sequential activation of the stacks, resulting in an improvement of the single stack efficiency over the lifetime and a slight increase in overall hydrogen production. Full article