NUST Institutions Library Catalogue Search for 'an:"119652"'

Forecasting Groundwater Consumption for an Urban Environment Using Machine Learning Techniques /

By Usama, Muhammad. . 47p. ; 30cm..

Place Hold on Forecasting Groundwater Consumption for an Urban Environment Using Machine Learning Techniques /

Numerical Investigation Of Flow Around Double, Single And No Slit Cylinder /

By Idrees, Hassaan. . 46p. , This study presents a numerical investigation of the flow characteristics around a single and double-slotted cylinder using computational fluid dynamics (CFD). The simulations were conducted using CFD software OpenFOAM on a structured grid. The governing equations for incompressible, laminar flow were solved using the SIMPLE FOAM solver. A mesh-independent study led to a structured grid with 0.7m cells which was sufficient to capture the flow features accurately. The results show that the addition of double slits to the cylinder significantly reduces the drag coefficient as compared to a normal cylinder. The reduction in drag coefficient was observed to be more significant for wider slits, with a maximum reduction of 22% observed for a gap between slits and a width-to-diameter ratio of 0.1. The streamlines and pressure contours for the double-slotted cylinder show a distinct change in the flow pattern due to the presence of the slits. The flow around the slotted cylinder is observed to be more streamlined with less vortex shedding as compared to the no-slit cylinder. Additionally, the slits help in reducing the size of the wake region behind the cylinder, resulting in a reduction of pressure drag. The effect of the Reynolds number on the flow characteristics was also studied. It was observed that the increase in Reynolds number resulted in a decrease in drag coefficient for each set of cases, similar to that of a no-slit and single-slotted cylinder. The results of this study suggest that the presence of double slits can significantly alter the flow characteristics of a cylinder and improve its aerodynamic performance, especially in laminar conditions. The findings of this study can have potential applications in various fields, including aerospace, wind energy, and marine engineering 30cm.

Place Hold on Numerical Investigation Of Flow Around Double, Single And No Slit Cylinder /

Forecasting of Irradiance on Bifacial Solar Panel based on Climate Parameters and Tilt Angle /

By Iqbal, Junaid . . 63p. , The aim of this study was to explore the use of data-driven approaches for designing net zero energy buildings using bifacial solar panels and deep learning models. The study utilized historical data on solar irradiance to forecast future irradiation levels, which were then used to optimize the tilt angle of bifacial solar panels for maximum energy collection. Two deep learning models, namely RNN-LSTM and transformers, were evaluated using various evaluation metrics, including MAE, RMSE, SMAPE, and R2. The results showed that both models were effective in forecasting irradiations with varying forecasting horizons, with the transformers model outperforming the RNN-LSTM model in terms of MAE values. The study provides insights into the use of data-driven approaches for designing net zero energy buildings, highlighting the potential of deep learning models in optimizing the use of renewable energy sources for sustainable development. The results show that both models were effective in forecasting irradiance, with R2 values of 0.927 for RNN-LSTM and 0.894 for Transformers. Additionally, the Transformers model outperformed the RNN-LSTM model with a lower range of MAE values from 0.05 to 0.03 across the same horizons. These findings suggest that deep learning models can effectively forecast solar irradiance and aid in optimizing the performance of net-zero energy buildings. 30cm.

Place Hold on Forecasting of Irradiance on Bifacial Solar Panel based on Climate Parameters and Tilt Angle /

Mathematical modeling of groundwater level in Islamabad based on experimental data /

By Luqman ,Muhammad. . 57p. ; 30cm..

Place Hold on Mathematical modeling of groundwater level in Islamabad based on experimental data /

Numerical Simulation of Fluid Flow and Heat Transfer in Enhance Twisted Elliptical Tubes /

By Nawaz, Mohtasim. . 43p. ; 30cm..

Place Hold on Numerical Simulation of Fluid Flow and Heat Transfer in Enhance Twisted Elliptical Tubes /

Computational Analysis of Heat Transfer and Pressure Drop in Helically Micro Finned Tubes /

By Ammar Ali ,Muhammad. . 47p. ; 30cm..

Place Hold on Computational Analysis of Heat Transfer and Pressure Drop in Helically Micro Finned Tubes /

Estimation of Drag Coefficients of Vehicle Platoons using Machine Learning /

By Jaffar ,Farwa . . 50p. ; 30cm..

Place Hold on Estimation of Drag Coefficients of Vehicle Platoons using Machine Learning /

SOLAR IRRADIANCE FORECASTING USING DEEP LEARNING AND STATISTICAL METHODS FOR ISLAMABAD /

By HAIDER ,SYED ALTAN. . 57p. ; 30cm..

Place Hold on SOLAR IRRADIANCE FORECASTING USING DEEP LEARNING AND STATISTICAL METHODS FOR ISLAMABAD /

Thermal Mapping of Indoor Building Surfaces /

By Khan, Abdul Hakeem . . 62p. , Thermal Scanning of indoor Environments is crucial for System Optimization, Energy efficiency and Human comfort. Utilizing thermal maps generated through a thermal scanning system allows for the Identification of Temperature variations, design enhancement, anomalies detection and HVAC system optimization. This data-driven approach supports cost-effective energy conservation and helps contribute to regulatory compliance, health and safety standards in indoor environments. The developed scanning system integrates development components to provide a comprehensive solution for diverse applications within confined spaces. The thermal mapping system incorporates multimodule system, prominent components that record/capture data are Thermal camera, pi/web camera and a proximity/distance sensor. The system is equipped with real time and offline data processing capabilities, mainly Image Processing, object detection and data generation. Key features of the system include its adaptability to various indoor settings, enabling to extract temperature measurements, object detection, distance measurement and detection of thermal signature. The system's design facilitates rapid and accurate detection of anomalies, such as hotspots or cold zones, contributing to enhanced safety and efficiency in indoor environments. Thermal imaging system's versatility extends to its compatibility with multiple platforms, including handheld devices, drones, and fixed installations, ensuring flexibility in deployment. utilize the system for applications ranging from building diagnostics and energy efficiency assessments to fire prevention and security monitoring. In conclusion, the developed thermal imaging system represents a significant advancement in indoor environmental scanning technology. Its versatility, efficiency and ease of use make it an invaluable tool for professionals across various industries, promoting enhanced safety, efficiency, and data-driven decision-making in indoor spaces. 30cm.

Place Hold on Thermal Mapping of Indoor Building Surfaces /

Artificial Intelligence Powered Sustainability in Solar and Wind Hybrid Energy Systems /

By Javaid, Ali . . 205p. , Increasing global energy demand and environmental concerns drive the shift to sustainable alternatives. Solar and wind energy, with their eco-friendly attributes and abundant availability, emerge as key contenders. However, effectively harnessing renewable energy faces challenges due to variability, intermittency, and the need to adapt energy systems to diverse environments. Engineers and planners face the unpredictability inherent in renewable resources influenced by weather conditions, seasonal changes, and geographical variations. As renewable energy grows in the energy mix, integrating fluctuating sources into the grid becomes complex, necessitating energy storage and grid management solutions. Pakistan aims for 16% solar and wind energy by 2040. Technological advancements, including artificial Intelligence (AI), Machine Learning (ML), and improved weather forecasting, enhance renewable energy predictions. Accurate forecasting is crucial for a stable power supply, requiring sophisticated models. Supply side forecasting, fundamental for energy planning, faces challenges due to the unpredictability of solar and wind. Various forecasting techniques, including Transformer, Long Short Term Memory (LSTM), Support Vector Regression (SVR), and Linear Regression (LR), have been explored to improve the accuracy of renewable energy forecasts. The thesis conducts two case studies of energy forecast in Islamabad. The first study focuses on wind speed prediction using LR, SVR, and LSTM models. LSTM emerges as the most effective, achieving 78% accuracy for a 2-day wind speed forecast. Mean absolute error (MAE) serves as the performance metric. Combining techniques optimize prediction accuracy, facilitating the integration of more renewable energy into the grid. Addressing intermittency, storing excess energy as hydrogen is proposed, 6.76 kg estimated hydrogen production per day using wind energy and Proton Exchange Membrane (PEM). Similarly in the second study, hybrid solar and wind energy systems exhibit similar trends, inspiring the exploration of alternative hybrid solutions. The Transformer model predicts energy production, achieving 90.7% accuracy for solar irradiance and 90.45% for wind speed. Additionally, the analysis of model behavior unveiled that the R2 score exhibited a direct correlation with the look-back period and epochs, while demonstrating an inverse relationship with training data, horizon, and learning rate. xiii In conclusion, the global shift to sustainable energy, driven by rising demand and environmental concerns, face challenges in efficiently harnessing renewable energy due to its variability. As countries like Pakistan aim to integrate more renewables into their energy mix, advancements in technology, particularly artificial intelligence and machine learning, play a critical role in improving the accuracy of energy predictions. Accurate supply side forecasting is essential for effective energy planning. In the context of hybrid systems such as solar and wind, the Transformer model stands out for its significant accuracy in predicting energy production. These advancements represent significant advances toward achieving resilience and sustainability in the energy sector. Furthermore, to relieve the challenges posed by intermittency, storing surplus renewable energy in the form of hydrogen is proposed as a promising and viable solution. 30cm.

Place Hold on Artificial Intelligence Powered Sustainability in Solar and Wind Hybrid Energy Systems /

Artificial Intelligence Powered Sustainability in Solar and Wind Hybrid Energy Systems/

By Javaid, Ali. . 186,p;

Place Hold on Artificial Intelligence Powered Sustainability in Solar and Wind Hybrid Energy Systems/

A Machine Learning-Driven Approach to Multi-Variable Solar Panel Health Monitoring /

By Iqbal, Saeed . . 167p. , The health of solar PV systems is vital for maintaining efficiency, reliability, and safety. Although PV systems are inherently low-maintenance and lack moving parts, their performance depreciates over time due to environmental stressors such as dust accumulation, temperature fluctuations, microcracks, and panel misalignment. Effective monitoring systems enable the early detection of these issues, helping timely initiating the maintenance procedures, improving energy production, and extending system lifespan. This is of particular significance since improving PV efficiency through proactive management is often more cost-effective than expanding capacity through additional investments. This study presents an innovative approach for evaluating solar panel health using ML-driven analysis, as an alternate of traditional multi-sensor methodologies. The proposed technique extracts multiple attributes from a single data stream, employing ML algorithms to predict environmental impacts on PV performance. Sensor data was analysed to find key characteristics that accurately represent various aspects of panel health. Initially, temperature effects on a 100 kW grid-tied solar system deployed across various installation sites, including parking lots, rooftops, and ground-level arrays were examined. The results indicated that variations in operational temperature led to power losses of 27.95% in ground-mounted systems, 5.41% in rooftop systems, and 0.82% in parking-based systems. These outcomes were derived from temperature measurements at the three installation sites, further supported by weather station data. TMR sensors were also investigated for their ability to check multiple PV panel variables, particularly threeaxis orientation. Experimental findings revealed that the largest axis error values for Bx, By, and Bz, the magnetic field components along the X, Y, and Z axes, were below 0.1%, 0.3%, and 0.5%, respectively. However, TMR sensors proved insufficient for accurately capturing solar panel temperature fluctuations, prompting a shift toward EIS for a more comprehensive analysis. EIS experiments were conducted under controlled temperature conditions ranging from 10°C to 60°C, while maintaining all other environmental variables constant. The EIS response was recorded for 26 distinct parameters, providing a detailed dataset for analysis. The 26 distinct parameters are various electrical values obtained from the EIS experiments. ML classification of the EIS data using Caret-based techniques foundxvii the Light Gradient Boosting Machine and CatBoost Classifier as the more suitable models, achieving classification accuracies of 85.63% and 85.54%, respectively. Additionally, EIS was used to detect microcracks and dust accumulation under four real-world scenarios: (1) clean and healthy panels, (2) dusty but healthy panels, (3) clean panels with microcracks, and (4) dusty panels with microcracks. The Gradient Boost classifier achieved an impressive 97.5% accuracy in distinguishing these conditions. The experimental results indicated that combining EIS with machine learning provides a powerful approach for assessing solar panel temperature variations, detecting microcracks, and monitoring dust accumulation through DC power lines alone. This method demonstrates considerable potential for real-time PV system diagnostics, thereby enhancing predictive maintenance and operational monitoring. 30cm.

Place Hold on A Machine Learning-Driven Approach to Multi-Variable Solar Panel Health Monitoring /

A Machine Learning-Driven Approach to Multi-Variable Solar Panel Health Monitoring/

By Iqbal, Saeed.

Place Hold on A Machine Learning-Driven Approach to Multi-Variable Solar Panel Health Monitoring/