28 research outputs found
Effect of 2023 European Heatwave on Photovoltaic Energy Generation: ACase Study of Central and Southern Italy
No full textHeatwaves can increase solar energy output due to increased irradiance levels. However, they can also result in decreased efficiency and probable long-term damage to photovoltaic panels. This research work investigates the impact of 2023 European heatwave on the energy generation of photovoltaic installations in central and southern Italy. Data collected from 98 inverters associated with three different plants spanning from 01-01-2017 to 20-08-2023 reveals distinctive performance trends among three plants during the summer of 2023 compared to previous years. Plants A and B experienced decreased energy generation, attributed to the extreme heatwave. In contrast, Plant C maintained consistent performance, benefiting from its elevated position and enhanced air circulation due to its geological location at Adriatic coast. Performance metrics based on IEC-61724 standards further support these findings. This study highlights the importance of strategic placement and design considerations for photovoltaic installations to mitigate the adverse effects of heatwaves
Deep learning augmented medium-term photovoltaic energy forecasting: A coupled approach using PVGIS and numerical weather model data
No full textIntegrating PV energy resources into energy grids is crucial for PV energy organizations, making medium- and short-term PV forecasts important. PV organizations look forward to modern tolls for efficient systems for the most beneficial operations for PV systems. This research proposes, applies, and assesses a modern machine learning and deep learning based short-term PV energy forecasting system. Numerical weather model-based data is utilized for real-time forecasting at an hourly scale for the next four days, an additional analysis is performed by leveraging the PVGIS data in addition to NWM data. The proposed methodology is developed and applied to more than 200 PV installations, both BIPVs and BAPVs. The system was able to produce effective PV energy forecasts with high accuracy and efficiency analysis of 3 different PV installations ranging 17 kWp, 91 kWp, and 386kWp are reported in this paper. The research concluded with the feasibility of the proposed systems and findings further support the efficacy of the proposed framework, which can be adopted by organizations seeking to optimize PV system performance and reliability
IDD-Net: A Deep Learning Approach for Early Detection of Dental Diseases Using X-Ray Imaging
No full textEarly detection of dental diseases such as cavities, periodontitis, and periapical infections is crucial for effective management and prevention, as these conditions can lead to severe complications if left untreated. However, traditional diagnostic methods are often manual, time consuming, and heavily reliant on expert judgment, which can introduce variability and delay in diagnosis. To address these critical challenges, we propose IDD-Net (Identification of Dental Disease Network), a novel deep learning-based model designed for the automatic detection of dental diseases using panoramic X-ray images. The proposed framework leverages Convolutional Neural Networks (CNN) to enhance the accuracy and efficiency of dental condition classification, thereby significantly improving the diagnostic process. In our comprehensive evaluation, IDD-Net’s performance is rigorously compared to four state-of-the-art deep learning models: AlexNet, InceptionResNet-V2, Xception, and MobileNet-V2. To tackle the issue of class imbalance, we employ the Synthetic Minority Over-sampling Technique with Tomek links (SMOTE Tomek), ensuring a balanced sample distribution that enhances model training. Experimental results showcase IDDNet’s exceptional performance, achieving a 99.97% AUC, 98.99% accuracy, 98.24% recall, 98.99% precision, and a 98.97% F1-score, thus outperforming benchmark classifiers. These findings underscore the transformative potential of IDD-Net as a reliable and efficient tool for assisting dental and medical professionals in the early detection of dental diseases. By streamlining the diagnostic process, IDD-Net not only improves patient outcomes but also has the potential to reshape standard practices in dental care, paving the way for more proactive and preventive
approaches in oral health management
Exploiting building information modeling and machine learning for optimizing rooftop photovoltaic systems
No full textThe primary objective of this study is to develop a strategy to maximize the potential of Building Applied Photovoltaics (BAPV) by providing researchers and experts in the field with the appropriate tools. By utilizing operational data from an operational roof -mounted BAPV system and incorporating Building Information Modeling (BIM) to improve its design and smooth integration into built settings, the study offers novel insights. A three-phase research methodology is applied, encompassing data collection and performance assessment, BIM modeling, and machine learning algorithms for energy forecasting. The case study involves a 160 kWp photovoltaic system in Abruzzo, Italy, over a three-year period. The research employs different performance metrics recommended by IEC 61724 to compare experimental and theoretical data. BIM simulations are exploited as they are crucial for identifying the correct design process. Furthermore, four cutting -edge machine learning algorithms are used to forecast daily energy production. The random forest is identified as the best model for forecasting and the effect of tuning of hyperparameters on the efficiency of all models is also reported. The research adds to the larger conversation on sustainable energy management and solutions by providing researchers involved in the design and improvement of BAPV systems with a solid foundation for future developments
Investigating the effects of hyperparameter sensitivity on machine learning algorithms for PV forecasting
No full textMachine Learning (ML) models have been introduced in the past, and users have debated whether to tune the hyperparameters of the models. This study investigates the effects of tuning the hyperparameters of the ML models and summarizes the models that are most sensitive to hyperparameter tuning. This study leveraged the historic energy production
data of two already operational PV plants. Four state-of-the-art ML models, namely Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) were investigated. All the ML models were trained with the same training features (meteorological estimates) obtained from the National Aeronautics and Space
Administration’s (NASA) Power project, with the daily PV energy production selected as the target variable. Models were developed and executed with default and tuned hyperparameters using an 85-15% traintest split. The results revealed that all the models showed improved performance with the tuned hyperparameters. However, the DT and SVR
models depicted significantly improved RMSE after tuning of the hyperparameters. The RMSE of DT improved from 111 kWh/d to 75 kWh/d for one plant and from 442 kWh/d to 270 kWh/d for the second plant after tuning the hyperparameters. Similarly, the RMSE of SVR improved from 59 kWh/d to 50 kWh/d in the first case, and in the second case, the improvement of RMSE from 536 kWh/d to 294 kWh/d was observed. The efficiency of the RF and KNN models also improved to some extent after tuning, but the RMSE closely agreed with the default hyperparameters in one case study, making the RF and KNN less prone to hyperparameter sensitivity. This study concluded with the finding that it is
necessary to tune the hyperparameters of the DT and SVR models, specifically for energy forecasting. Moreover, the results of this study also highlight the significance of meteorological estimates from NASA’s Power project, as models successfully discerned the complex energy forecast patterns. The dataset is deemed suitable for energy forecasting for areas with sparse ground-based observatories and may serve as a baseline dataset
for training the ML models
AI-Powered Advanced Technologies for a Sustainable Built Environment: A Systematic Review on Emerging Challenges
No full textThe integration of digital technologies with Artificial Intelligence could serve as a strategic approach to achieving the goals set by the European Union, mainly concerning sustainability, carbon emission reduction, and digitalization in the construction sector. In this regard, this paper aims to examine the major trends in the application of AI integrated with digital technologies to boost the environmental sustainability of the built environment throughout
its life cycle. A systematic literature review was conducted, in accordance with the PRISMA
guidelines, inspecting the Scopus database from 2015 to 2025. After having applied specific
exclusion and inclusion criteria, 102 studies have been examined to identify key trends and
transformative innovations enhancing sustainable approaches for the built environment.
The results have been systematized based on the phases of the building life cycle which
are impacted most by AI-powered digital technologies, and on sustainability areas that
are attracting the greatest attention. The main research gaps are identified in the limited
exploration of renovation and end-of-life phases of the life cycle, in the lack of technologies interoperability, in data complexity and quality issues, in a lack of cost-effective solutions, and in limited regulation and standardization
Hybrid ML/DL Approach to Optimize Mid-Term Electrical Load Forecasting for Smart Buildings
No full textMost electric energy consumption in the building sector is provided by fossil fuels, leading to high greenhouse gas emissions. However, the increasing need for sustainable infrastructure has triggered a significant trend toward smart buildings, which enable optimal and efficient resource usage. In this context, accurate mid-term energy load forecasting is crucial for energy management. This study proposes a hybrid forecasting model obtained through the combination of machine learning (ML) and deep learning (DL) approaches designed to enhance forecasting accuracy at an hourly granularity. The hybrid two-layer architecture first investigates the model's performance individually, such as decision tree (DT), random forest (RF), support vector regression (SVR), Extreme Gradient Boosting (XGBoost), FireNet, and long short-term memory (LSTM), and then combines them to leverage their complementary strengths in a two-layer hybrid design. The performance of these models is assessed on smart building energy datasets with weather data, and their accuracy is measured through performance metrics such as mean squared error (MSE), root mean squared error (RMSE), and R-squared (R2). The collected results show that the XGBoost outperformed other ML models. However, the hybrid model obtained by combining FireNet and XGBoost models delivers the highest overall accuracy for the performance parameters. These findings highlight the effectiveness of hybrid models in terms of prediction accuracy. This research contributes to reliable energy forecasting and supports environmentally sustainable practices
Penetration Testing of Android-based Smartphones
No full textThe purpose of this work has been to perform a security analysis of Android-based
Smartphones. Smartphone usage and adaptation are increasing day by day with a variety of
applications. These applications can be very critical in nature such as mobile banking, and
mobile payment systems and users are often unknowing about the security risks involved in
such applications.
Android, an open source operating system, is rapidly increasing in the Smartphone industry.
It has already beaten the most popular mobile operating systems, like RIM, iOS, Windows
Mobile and even Symbian, which ruled the mobile market for more than a decade.
In this thesis, we have analysed the architecture of the Android operating system and tested
its security through penetration testing. We have picked the most popular and recommended
tools to test the security in the TCP/IP suite and different attacks have been performed on
three different Android versions. The thesis also contains a discussion about our findings,
how secure the Android system is and how much trust can be placed on it while using it
GENDER DIFFERENCES IN THE SPEECH OF MEN AND WOMEN: AN ANALYSIS OF SPORTS ARTICLES
No full textThe present research paper aims to investigate how men and women use language differently in daily life. It is observed that both men and women use language differently. In the present research, the researcher collected data from different newspapers. The researcher has selected twenty-eight sports articles written by male and female sports journalists on male and female tennis players. The researcher used “Ant con” software to interpret the data. The final result shows that women writer uses polite Language, empty adjectives, articles, singular pronouns ‘I,’ prepositions, intensifiers and emotional words, whereas men use directive, supportive, informative and strong words
