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Eng. Proc., 2026, Designs 2026

The 1st International Online Conference on Designs

Online | 9–10 February 2026

Volume Editor:
Wenbin Yu, Shandong University, Jinan, China

Number of Papers: 16
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Cover Story (view full-size image): The 1st International Online Conference on Designs (Designs 2026) provided an interactive platform for researchers all over the world to discuss achievements as well as challenges in the design of [...] Read more.
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15 pages, 1454 KB  
Proceeding Paper
Physics-Regularized Neural Networks for Photovoltaic Power Prediction Under Limited Experimental Data
by Aswin Karkadakattil
Eng. Proc. 2026, 138(1), 1; https://doi.org/10.3390/engproc2026138001 - 20 Apr 2026
Viewed by 1153
Abstract
Accurate photovoltaic (PV) power prediction under limited experimental data remains a significant challenge, particularly when purely data-driven models generate predictions that violate fundamental physical constraints. This study proposes a physics-regularized neural network framework for data-efficient PV power modeling using only 45 real experimental [...] Read more.
Accurate photovoltaic (PV) power prediction under limited experimental data remains a significant challenge, particularly when purely data-driven models generate predictions that violate fundamental physical constraints. This study proposes a physics-regularized neural network framework for data-efficient PV power modeling using only 45 real experimental measurements of irradiance and temperature. To address data sparsity while preserving physical realism, a physics-guided synthetic augmentation strategy is introduced to generate additional training samples strictly within experimentally validated operating bounds. The proposed Physics-Informed Neural Network (PINN) incorporates two complementary physical constraints directly into the training objective: (i) enforcement of the Shockley–Queisser thermodynamic efficiency limit to maintain compliance with theoretical conversion bounds and (ii) monotonicity regularization to ensure non-negative power gradients with respect to irradiance. Unlike conventional post-processing correction methods, these physical constraints are embedded during model training, enabling simultaneous improvement in predictive accuracy and physical consistency. When benchmarked against a structurally identical unconstrained Artificial Neural Network (ANN), the proposed framework achieves strong predictive performance (R2 = 0.9947, RMSE = 5.21 W) while reducing monotonicity violations by approximately 82%. Robustness evaluations under extrapolated irradiance conditions and elevated temperature scenarios further demonstrate stable and physically admissible behavior beyond the training domain. Overall, the results demonstrate that integrating limited experimental measurements with embedded physical priors enables reliable and physically consistent PV power prediction in sparse-data environments, highlighting the potential of physics-regularized learning for renewable energy modeling applications. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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8 pages, 3306 KB  
Proceeding Paper
Automated Response Surface Methodology: Computational Replication and Validation Framework for Optimizing Supercapattery Materials
by Thiago Ferro de Oliveira and Simoni Margareti Plentz Meneghetti
Eng. Proc. 2026, 138(1), 2; https://doi.org/10.3390/engproc2026138002 - 20 Apr 2026
Viewed by 795
Abstract
Combining Response Surface Methodology (RSM) with Central Composite Design (CCD) is a powerful statistical approach to optimizing materials in energy storage systems. This study presents an open-source Python (v3.8+) framework that replicates and validates the RSM-based optimization of NiCo2S4–graphene [...] Read more.
Combining Response Surface Methodology (RSM) with Central Composite Design (CCD) is a powerful statistical approach to optimizing materials in energy storage systems. This study presents an open-source Python (v3.8+) framework that replicates and validates the RSM-based optimization of NiCo2S4–graphene supercapattery materials. We validated the framework by replicating a 20-experiment CCD analyzing graphene/NCS ratios, hydrothermal time, and S/Ni molar ratios. Advanced optimization using the Differential Evolution algorithm was integrated to efficiently solve the high-dimensional response surface space. The model explained 97.16% of the variance, and comprehensive diagnostic tests confirmed the assumptions of normality and residual independence. This approach provides an open-source methodology that supports reproducible and scalable data-driven material design and facilitates transparent computational materials science studies. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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12 pages, 4066 KB  
Proceeding Paper
Advancements in Artificial Intelligence for Renewable Energy Systems over the Past Decades
by Md. Nurjaman Ridoy and Sk. Tanjim Jaman Supto
Eng. Proc. 2026, 138(1), 3; https://doi.org/10.3390/engproc2026138003 - 24 Apr 2026
Viewed by 984
Abstract
Sunlight, air, and other natural resources are invaluable gifts that must be utilized responsibly to enhance human welfare while preserving the environment and protecting all forms of life. The reliance on fossil fuels has increasingly threatened these resources, which has made the exploration [...] Read more.
Sunlight, air, and other natural resources are invaluable gifts that must be utilized responsibly to enhance human welfare while preserving the environment and protecting all forms of life. The reliance on fossil fuels has increasingly threatened these resources, which has made the exploration of sunlight and wind energy as major renewable energy sources a critical focus of research and development. Artificial intelligence (AI), originally developed to mimic human thought and decision-making processes, has become a transformative force in renewable energy systems by optimizing energy generation, management, and distribution for greater efficiency and sustainability. This paper shows the evolution of AI applications in wind, solar, geothermal, hydro, bioenergy, and hybrid energy systems over the last few decades. A bibliometric analysis of the literature was conducted systematically by reviewing relevant journal articles between 2000 and 2025. The analysis identifies research trends, collaboration patterns, emerging domains, and future directions. Different studies show that AI technologies’ capabilities improve several aspects of renewable energy for the purpose of integrating operations into the grid for users, specifically forecasting, improving system stability and frequency, and enabling transient stability assessment. The study highlights key challenges and provides high-level insights to guide future research and support the continued development and application of AI in renewable energy systems. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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15 pages, 873 KB  
Proceeding Paper
AI-Enhanced Strategies for Energy-Efficient Urban Environments
by Sk. Tanjim Jaman Supto and Md. Nurjaman Ridoy
Eng. Proc. 2026, 138(1), 4; https://doi.org/10.3390/engproc2026138004 - 7 May 2026
Viewed by 988
Abstract
Artificial intelligence (AI) is rapidly redefining the management of urban energy systems by coupling predictive analytics with closed-loop control across buildings, power grids, and mobility networks, positioning cities as critical leverage points in global decarbonization efforts. Contemporary urban environments generate vast, heterogeneous datasets [...] Read more.
Artificial intelligence (AI) is rapidly redefining the management of urban energy systems by coupling predictive analytics with closed-loop control across buildings, power grids, and mobility networks, positioning cities as critical leverage points in global decarbonization efforts. Contemporary urban environments generate vast, heterogeneous datasets that enable advanced machine learning applications; however, limitations remain, including interpretability–fairness trade-offs, fragmented data governance, interoperability gaps, cybersecurity risks, and insufficient long-term validation across diverse climatic and socio-economic contexts. This review evaluates AI-driven strategies for energy-efficient urban systems and identifies the technical and governance conditions required for scalable impact. The evidence synthesized indicates that supervised and ensemble learning models achieve high predictive accuracy for electricity demand and chiller performance, with models such as Random Forest Regression achieving R2 values up to 0.9835 in electricity consumption forecasting, while unsupervised approaches detect latent inefficiencies in HVAC systems, delivering measurable savings typically around 6% under controlled benchmarking conditions. Deep learning architectures improve multi-building forecasting and real-time control, with hybrid CNN–LSTM models achieving prediction accuracies up to 97% and outperforming traditional statistical approaches in weekly energy demand forecasting achieving higher prediction accuracy and significant energy savings in complex urban subsystems with reported reductions of approximately 21–23% in residential and educational buildings and up to 37% in office HVAC systems. Hybrid and physics-informed AI models embed thermodynamic principles into data-driven frameworks, improving robustness, interpretability, and generalization. IoT sensor networks and edge-computing architectures support adaptive HVAC, demand–response, and smart-grid management, while integrated building–grid–mobility systems enhance load balancing, storage use, and carbon reduction. AI-enhanced strategies offer a credible pathway toward measurable reductions in urban energy use and emissions with deep reinforcement learning in digital twin environments reducing HVAC energy demand by 10–35% while maintaining thermal comfort within ±0.5 °C in line with ASHRAE standards, and overall energy savings reaching up to 44% in optimized systems when supported by interoperable infrastructures, secure digital-twin architectures, and standardized measurement and verification protocols. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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9 pages, 1122 KB  
Proceeding Paper
Multi-Objective Evolutionary Prediction with an Artificial Intelligence-Based Approach for Urban Energy Planning
by Md Rakibul Islam, Aritra Islam Saswato and Md Salah Uddin
Eng. Proc. 2026, 138(1), 5; https://doi.org/10.3390/engproc2026138005 - 26 May 2026
Viewed by 521
Abstract
This study investigates the relationship between weather conditions (temperature, humidity), air pollutants (PM2.5, PM10, and CO), and photovoltaic (PV) degradation characteristics using location-specific machine learning frameworks. A data augmentation technique was employed to enhance the predictive modeling datasets. The [...] Read more.
This study investigates the relationship between weather conditions (temperature, humidity), air pollutants (PM2.5, PM10, and CO), and photovoltaic (PV) degradation characteristics using location-specific machine learning frameworks. A data augmentation technique was employed to enhance the predictive modeling datasets. The research evaluates four machine learning models: AdaBoost, Gradient Boosting, Decision Tree, and Random Forest. We found strong regression analysis values using the addressed machine learning models. Furthermore, feature importance analysis reveals that PM2.5 has the most significant impact on PV module degradation. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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10 pages, 2706 KB  
Proceeding Paper
Modelling and MATLAB-Based Optimisation of Carbon Dioxide Adsorption Using Zn-MOF-5
by Shonisani Salvation Muthubi, Dorcas Museme Mabulay and Pascal Kilunji Mwenge
Eng. Proc. 2026, 138(1), 6; https://doi.org/10.3390/engproc2026138006 - 22 May 2026
Viewed by 823
Abstract
The growing concern over greenhouse gas emissions has prompted the need for efficient carbon dioxide (CO2) capture technologies. This study focuses on simulating CO2 adsorption using a zinc-based metal–organic framework (Zn-MOF-5). The primary aim is to develop and refine a [...] Read more.
The growing concern over greenhouse gas emissions has prompted the need for efficient carbon dioxide (CO2) capture technologies. This study focuses on simulating CO2 adsorption using a zinc-based metal–organic framework (Zn-MOF-5). The primary aim is to develop and refine a robust MATLAB-based approach for equilibrium and kinetic modelling using the Linear Driving Force (LDF) model and Langmuir isotherm, capable of accurately predicting CO2 adsorption performance under varying operational conditions. By employing advanced computational methods, this research seeks to streamline the process design and enhance the feasibility of sustainable CO2 capture solutions. Excel was used for statistical analysis and validation, while MATLAB R2025a was utilised for equilibrium and kinetic modelling using the LDF model and the Langmuir isotherm. The independent effects of temperature, pressure, and flow rate were evaluated using the variable effect method. The study found a significant negative association between temperature and CO2 uptake, consistent with the exothermic nature of the adsorption process. Pressure had a significant impact on adsorption, whereas flow rate had little effect within the investigated range. The simulated CO2 uptake (21.196 mmol/g) closely matched the experimental data (21.07 mmol/g) with a 0.59% variance, validating the model’s trustworthiness. The research shows that Zn-MOF-5 has a strong adsorption potential and that simulation tools can significantly minimise experimental costs and time. Furthermore, it underscores the potential of simulation tools to significantly reduce experimental costs and time, paving the way for more efficient and effective carbon capture solutions. This initiative not only contributes to optimising process design but also promotes sustainable practices in addressing global CO2 emissions. By contributing to process optimisation, this study aligns with the United Nations Sustainable Development Goal (SDG) 13: Climate Action, which emphasises the urgent need for innovative solutions to combat climate change and its impacts. Furthermore, it promotes sustainable practices to address global CO2 emissions, thereby supporting broader efforts for environmental sustainability. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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15 pages, 2816 KB  
Proceeding Paper
The Role of Artificial Intelligence in Driving Renewable Energy Transition: From the Current Landscape to Future Pathways
by Md. Nurjaman Ridoy, Sk. Tanjim Jaman Supto, Gaurob Saha and Sabbir Hossain
Eng. Proc. 2026, 138(1), 7; https://doi.org/10.3390/engproc2026138007 - 22 May 2026
Viewed by 2049
Abstract
The shift from fossil fuels to renewable energy is a key component in achieving global sustainability and dealing with climate change. Natural resources, such as sunlight, air, water, and biomass, have tremendous potential to create clean energy; however, exploiting these resources in an [...] Read more.
The shift from fossil fuels to renewable energy is a key component in achieving global sustainability and dealing with climate change. Natural resources, such as sunlight, air, water, and biomass, have tremendous potential to create clean energy; however, exploiting these resources in an efficient, stable, and large-scale integration manner is difficult due to their variable and distributed nature. Artificial intelligence (AI) approaches that mimic human learning and decision-making have recently become viable approaches to solving renewable energy problems. This study mainly examines the current landscape of AI applications across solar, wind, hydro, geothermal, ocean, hydrogen, bioenergy, and hybrid energy systems. AI enhances renewable energy forecasting, improves power system frequency analysis and stability assessments, and optimizes dispatch and distribution networks. Beyond technical optimization, AI also contributes to broader sustainability goals, including energy efficiency improvements, intelligent smart grid management, and enabling mechanisms such as carbon trading and circular economy practices to reduce exposure to climate extremes. Drawing on evidence from a range of renewable energy areas, this review highlights the importance of AI in bridging the link between technological innovation and sustainable energy management. This paper discusses potential future research avenues, such as building sophisticated AI designs, energy-efficient chips, and data communication networks. Ultimately, the synergy between AI and renewable energy systems represents not only a technological advancement but also a transformative pathway toward a resilient, low-carbon future. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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10 pages, 1570 KB  
Proceeding Paper
Circular Design as a Key Strategy to Cut Embodied Energy: A Digital AI Tool to Support Materials and Data Exchange for a Sustainable Built Environment
by Gabriele Rossini, Paola Altamura and Serena Baiani
Eng. Proc. 2026, 138(1), 8; https://doi.org/10.3390/engproc2026138008 - 29 May 2026
Viewed by 366
Abstract
The NPRR research project “From waste to manufacturing” developed an AI-powered digital tool to support the transition towards a circular built environment in Italy. The tool integrates a web platform enabling data exchange about materials with recycled content between designers, manufacturers and waste [...] Read more.
The NPRR research project “From waste to manufacturing” developed an AI-powered digital tool to support the transition towards a circular built environment in Italy. The tool integrates a web platform enabling data exchange about materials with recycled content between designers, manufacturers and waste recyclers, with a CAD plug-in for real-time sustainability assessment. As such, the tool fosters the use of recycled materials and allows a reduction in embodied energy. AI, trained through scenario-based learning and stakeholder participation, assists designers in sourcing recycled materials and processing data. With further training by LCA experts, it could interpret Environmental Product Declarations to guide material selection in line with international regulations. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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15 pages, 4192 KB  
Proceeding Paper
Adaptive Neuro-Fuzzy Control of a Small Wind Turbine–Battery DC Microgrid for Remote Electrification in Uzbekistan
by Botir Usmonov, Ulugbek Muinov, Komil Usmanov and Nigina Muinova
Eng. Proc. 2026, 138(1), 9; https://doi.org/10.3390/engproc2026138009 - 1 Jun 2026
Viewed by 405
Abstract
Rural regions of Uzbekistan experience continuing issues of energy access because of poor grid networks and variable renewable sources. The solution is small-scale wind turbines and energy storage. But the wind speeds and load demand are variable, and thus this solution needs intelligent [...] Read more.
Rural regions of Uzbekistan experience continuing issues of energy access because of poor grid networks and variable renewable sources. The solution is small-scale wind turbines and energy storage. But the wind speeds and load demand are variable, and thus this solution needs intelligent control systems to perform its best. This paper is an attempt to design an adaptive neuro-fuzzy inference system (ANFIS) controller to control a small wind power system with a battery storage unit. The controller will be intelligent to control the flow of power between the wind turbine, battery, and local loads. A model of MATLAB/Simulink is created to simulate the reaction of the system to various wind and load conditions. The simulation results indicate that the ANFIS controller improves voltage regulation, reduces power fluctuations, and enhances battery charge–discharge performance compared to the conventional PI controller. Environmental variability is effectively responded to by the system, making it more reliable and energy-efficient. ANFIS control and wind–battery microgrid integration provides a feasible and expandable off-grid electrification solution to remote areas. This strategy promotes the renewable energy ambitions of Uzbekistan and offers an example of smart microgrid implementation in other resource-limited rural areas. The next steps would be towards practical applications and hardware verification. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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13 pages, 4239 KB  
Proceeding Paper
Analysis of Power System Stability Indices Concerning High Penetration of Renewable Energies
by Amel Brik, Nour El Yakine Kouba and Ahmed Amine Ladjici
Eng. Proc. 2026, 138(1), 10; https://doi.org/10.3390/engproc2026138010 - 1 Jun 2026
Viewed by 984
Abstract
Currently, the large-scale integration of renewable energy sources (RESs), such as wind turbines and photovoltaic array, is profoundly altering the dynamic behavior of power systems. In particular, the reduction in system inertia makes transient stability more critical and increases the sensitivity of the [...] Read more.
Currently, the large-scale integration of renewable energy sources (RESs), such as wind turbines and photovoltaic array, is profoundly altering the dynamic behavior of power systems. In particular, the reduction in system inertia makes transient stability more critical and increases the sensitivity of the network to disturbances. The originality of this work lies in the systematic analysis of the nonlinear dynamics of power systems by thoroughly examining the impact of RESintegration on system stability, particularly through frequency response and voltage profile. In this context, a methodology for the evaluation and optimization of power system stability was proposed, based on two key indicators: the Critical Clearing Time (CCT) and the Rate of Change of Frequency (RoCoF). The IEEE 39-bus test system was used as a benchmark to simulate different scenarios. Three-phase faults are applied to determine the corresponding CCT values and to assess the system’s ability to regain a stable operating state after a severe disturbance. In addition, RoCoF variations are analyzed to quantify the impact of RES penetration on the frequency stability of the network. The obtained results show that a high penetration of renewable energy sources tends to reduce the CCT and increase the RoCoF, indicating a reduction in the dynamic robustness of the system. These observations are confirmed through comparative simulations performed with and without renewable energy integration. In conclusion, this study highlights the importance of optimal placement of renewable generation units, as well as the use of the CCT and RoCoF indices as effective diagnostic and optimization tools for modern power systems characterized by a high penetration of renewable energy sources. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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10 pages, 3469 KB  
Proceeding Paper
Development of a Framework for Using AI in Building Façade Optimization: An Application Focusing on Retrofitting NYCHA Midrise Housing in New York City
by Beatriz Bordignon Cypriano
Eng. Proc. 2026, 138(1), 11; https://doi.org/10.3390/engproc2026138011 - 3 Jun 2026
Viewed by 299
Abstract
Artificial intelligence (AI) technology and the Industrial Revolution (4IR) have the potential to rapidly advance smart buildings, materials, and construction processes to meet global decarbonization goals. Current retrofit techniques can vary a great deal in terms of their methodology from place to place, [...] Read more.
Artificial intelligence (AI) technology and the Industrial Revolution (4IR) have the potential to rapidly advance smart buildings, materials, and construction processes to meet global decarbonization goals. Current retrofit techniques can vary a great deal in terms of their methodology from place to place, but most have in common time constraints, budgets, and the need to reduce energy usage. The objective of this study is to develop a framework that optimizes the façade retrofitting process with the help of AI, bringing it in as a decision-making tool that also accounts for other parameters, but differently to traditional retrofit methodologies, the tenants are the judges. Thus, this research explores the possibilities of applying the AI tool Midjourney for problem solving in retrofitting in the design stages of the process. The overall framework is developed by addressing materiality, high performance analysis, affordable costs, and user experience inputs. The effectiveness of the framework is tested for three selected façade system options in terms of their Energy Use Intensity (EUI) simulated on Grasshopper, showcasing a final EUI reduction for options A, B and C analyzed at 32%, 5.8% and 26%, respectively. In addition, Life Cycle Analysis (LCA) and the overall costs were also simulated and compared across options. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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8 pages, 1663 KB  
Proceeding Paper
From Solar Panels to AI Decisions: Intelligent Server Utilization for Sustainable Computing
by Nikolaos Fragkos, Stylianos Katsoulis, Evangelos Nannos, Fotios Zantalis, Ioannis Chrysovalantis Panagou, Panagiotis Tsiakas and Grigorios Koulouras
Eng. Proc. 2026, 138(1), 12; https://doi.org/10.3390/engproc2026138012 - 25 Jun 2026
Viewed by 588
Abstract
Renewable integration is increasingly important for sustainable off-grid computing. The inherent variability of solar output frequently produces unusable midday surpluses. Leveraging recent Artificial Intelligence (AI) advances and established literature, we evaluate an AI-driven demand-response framework for scaling Large Language Models (LLMs) training servers [...] Read more.
Renewable integration is increasingly important for sustainable off-grid computing. The inherent variability of solar output frequently produces unusable midday surpluses. Leveraging recent Artificial Intelligence (AI) advances and established literature, we evaluate an AI-driven demand-response framework for scaling Large Language Models (LLMs) training servers using real-time solar energy data, Solcast forecasts, and battery storage records collected from Battery Management Systems (BMS), Maximum Power Point Tracking (MPPT) units, and smart inverters. An n8n AI Agent using the Ollama chat model gpt-oss:20b assesses surplus solar energy, activating selected servers to utilize otherwise wasted capacity. Workloads consistently align with solar availability, demonstrating 99% operational reliability, sub-second responsiveness, and accurate surplus-energy detection. This research demonstrates how Artificial Intelligence can repurpose surplus solar output into usable computational capacity, thereby contributing to a broader transition toward renewable-powered infrastructures. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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20 pages, 5122 KB  
Proceeding Paper
Resource-Significant Activity Costing in Offshore Structure Construction Projects Using Artificial Neural Network
by Mofiyinfoluwa Tobi Olowe and Michael Ayomoh
Eng. Proc. 2026, 138(1), 13; https://doi.org/10.3390/engproc2026138013 - 7 Jul 2026
Viewed by 413
Abstract
Fixed-bottom or floating offshore structures are the foundations, platforms, and associated infrastructure that allow for oil and gas production systems, offshore wind turbines, and cabling. The remote nature of these structures and the harsh environment with high variability in wind, waves, currents, and [...] Read more.
Fixed-bottom or floating offshore structures are the foundations, platforms, and associated infrastructure that allow for oil and gas production systems, offshore wind turbines, and cabling. The remote nature of these structures and the harsh environment with high variability in wind, waves, currents, and weather make construction activity very difficult and unpredictable; the cost of variation in the schedule can lead to high construction vessel and personnel costs. The adoption of artificial intelligence using trends observed in historical data can help achieve more accurate construction costs and schedule predictions, reducing the capital expenditure cost of installation. A resource-significant activity, sometimes called a resource-critical activity or high-resource-demand activity, is an activity on a construction or project schedule that consumes a disproportionately large share of one or more resources compared with others. Plant Design Modelling (PDM) is a digital process that creates and manages a detailed 3D model of a building’s physical and functional characteristics and semantic information, such as cost and schedule. PDM serves as a single source of truth for multidisciplinary activities and, therefore, serves as a rich data source for various construction applications, including project scheduling and cost estimation. Neural networks (NNs), a subset of machine learning algorithms inspired by the human brain, excel at identifying patterns in complex datasets and making predictions, such as forecasting costs based on non-linear relationships and historical trends. Data from an offshore structure modification project were extracted from Aveva’s Everything PDM, focusing on installation activities to create a dataset for machine learning model training. The structured data extracted exhibit non-linear patterns; therefore, linear, regularised linear, robust linear, and the ensemble (tree-based) models and supervised neural network models with varied architecture and hyperparameter values were evaluated and compared. The best performance was obtained using the deep-optimised ANN model. The result obtained is consistent with previous studies. The neural network models show a superior ability to predict the non-linear nature of offshore construction activities’ time. Full article
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8 pages, 2374 KB  
Proceeding Paper
Optimizing Offshore Green Hydrogen Systems via Modular Simulation
by Alvaro García-Ruiz, Pablo Fernández-Arias, Antonio del Bosque and Diego Vergara
Eng. Proc. 2026, 138(1), 14; https://doi.org/10.3390/engproc2026138014 - 9 Jul 2026
Viewed by 384
Abstract
This study presents a mathematics-based simulation model for designing, analyzing, and optimizing offshore green hydrogen stations powered by solar photovoltaic systems, applicable to any location worldwide. Developed in Python, the model integrates environmental, physical, and technological parameters to simulate and forecast hydrogen production [...] Read more.
This study presents a mathematics-based simulation model for designing, analyzing, and optimizing offshore green hydrogen stations powered by solar photovoltaic systems, applicable to any location worldwide. Developed in Python, the model integrates environmental, physical, and technological parameters to simulate and forecast hydrogen production via water electrolysis using alkaline (ALK) or proton exchange membrane (PEM) electrolyzers, combined with an adiabatic compressor that enhances energy storage and facilitates integration into smart grids. The five-phase modular methodology includes timeframe definition; estimation of solar electricity generation based on solar trajectory and the geographic orientation of photovoltaic panels; performance modeling of electrolyzers and compressors; and the integration of all components into a cohesive system. A case study demonstrates the model’s real-world applicability. Results from the Gulf of Cadiz case study show a substantial increase in solar energy capture in offshore environments due to reduced atmospheric pollution and sea-surface reflection. The reflected component is modeled as a function of sea-surface flatness. This reflection increases the daily average solar irradiance received by the photovoltaic panels by 8.44%. Under the modeled 2026 conditions and equivalent irradiance levels, the ALK electrolyzer produces 3.347% more hydrogen than the PEM electrolyzer. In addition, a 20% increase in electrolyzer efficiency raises hydrogen production by 32.35%, whereas the same increase in compressor efficiency improves production by 0.758%. These impacts directly correlate with proportional reductions in the photovoltaic panel surface area, driven by increased electricity generation capacity, which translates into smaller infrastructure needs. The model enables quantitative evaluation of trade-offs among solar irradiance, component performance, and system design. It supports cost reduction through optimized sizing and improved integration. This approach contributes to lowering the Levelized Cost of Electricity (LCOE) and promoting the viability of marine-based green hydrogen deployment. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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10 pages, 1635 KB  
Proceeding Paper
AI-Enhanced Detection of Thermal Anomalies in Urban Roofs via Drone-Assisted Infrared Thermography (UAV-IRT)
by Xiaojia Zhang, Andrea Garzulino and Elena Lucchi
Eng. Proc. 2026, 138(1), 15; https://doi.org/10.3390/engproc2026138015 - 20 Jul 2026
Viewed by 450
Abstract
This study investigates automated detection of rooftop thermal anomalies using Unmanned Aerial Vehicle-Based Infrared Thermography (UAV-IRT) and deep learning object detection models. UAV thermal images are used to identify potential thermal bridges on building rooftops. The experiment is framed as a preliminary workflow [...] Read more.
This study investigates automated detection of rooftop thermal anomalies using Unmanned Aerial Vehicle-Based Infrared Thermography (UAV-IRT) and deep learning object detection models. UAV thermal images are used to identify potential thermal bridges on building rooftops. The experiment is framed as a preliminary workflow validation performed on a subset of the Karlsruhe reference dataset. The study evaluates four YOLO models, including YOLOv9, YOLOv10, YOLOv11, and YOLOv12. The results indicate that all models can detect major thermal anomaly regions in UAV thermal imagery. YOLOv9 achieves slightly higher detection accuracy in the tested dataset. These results demonstrate the feasibility of the proposed workflow under reference-dataset conditions, while validation on a dedicated historic-building dataset remains necessary before claiming applicability to heritage or historic urban environments. Full article
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16 pages, 6497 KB  
Proceeding Paper
Spatial Assessment of a Proxy-Based Urban Energy Intensity Index Using Remote Sensing and AHP-MCDA: A Case Study of Dhaka City
by Sk. Tanjim Jaman Supto, Md. Nurjaman Ridoy and Md Kaium Hossain
Eng. Proc. 2026, 138(1), 16; https://doi.org/10.3390/engproc2026138016 - 1 Aug 2026
Viewed by 215
Abstract
Rapid urbanization and unplanned land development have transformed Dhaka into one of the most densely built metropolitan areas in South Asia, leading to increased surface heat accumulation and growing pressure on urban energy systems. Numerous studies have reported a progressive rise in Dhaka’s [...] Read more.
Rapid urbanization and unplanned land development have transformed Dhaka into one of the most densely built metropolitan areas in South Asia, leading to increased surface heat accumulation and growing pressure on urban energy systems. Numerous studies have reported a progressive rise in Dhaka’s Land Surface Temperature (LST), accompanied by a strong negative correlation between the Normalized Difference Vegetation Index (NDVI) and LST and a positive correlation between the Normalized Difference Built-Up Index (NDBI) and LST. However, spatially explicit assessments that integrate multiple geospatial proxies to characterize urban energy-intensity patterns remain limited, particularly in data-scarce cities where direct energy-consumption data are unavailable. The present study aims to develop and map a proxy-based Urban Energy Intensity Index (UEI) for Dhaka by integrating remote sensing, GIS, and AHP-MCDA techniques. Landsat 9 imagery and VIIRS nighttime light data were processed to derive NDVI, NDBI, and LST layers, while building footprints and road networks were extracted from OpenStreetMap to represent urban form. Eight environmental, built-environment, and anthropogenic indicators were standardized and weighted using the Analytic Hierarchy Process (AHP) before integration through a GIS-based weighted overlay approach. Results show that dense, impervious, and intensely illuminated built-up cores exhibit elevated LST and UEI values, while peri-urban and vegetated areas consistently display lower values, producing spatially distinct high-UEI clusters concentrated within Dhaka’s urban core. Because the index is derived from proxy indicators and was not validated against observed energy-consumption data, the findings should be interpreted as relative spatial patterns rather than direct measures of energy use or energy efficiency. These findings demonstrate the effectiveness of integrating remotely sensed and geospatial indicators to capture intra-urban variability in proxy-based urban energy intensity. The study provides a scalable and data-efficient framework for identifying priority zones for urban planning interventions and supports targeted strategies such as urban greening, surface albedo enhancement, and sustainable land-use planning to mitigate thermal stress. Full article
(This article belongs to the Proceedings of The 1st International Online Conference on Designs)
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