Daixuan Zhou¹, Yujin Liu¹, Xu Wang², Fuxing Wang¹, Yan Jia^2,*

Energy Engineering, Vol.121, No.12, pp. 3573-3616, 2024, DOI:10.32604/ee.2024.055853 - 22 November 2024

Abstract With the increasing proportion of renewable energy in China’s energy structure, among which photovoltaic power generation is also developing rapidly. As the photovoltaic (PV) power output is highly unstable and subject to a variety of factors, it brings great challenges to the stable operation and dispatch of the power grid. Therefore, accurate short-term PV power prediction is of great significance to ensure the safe grid connection of PV energy. Currently, the short-term prediction of PV power has received extensive attention and research, but the accuracy and precision of the prediction have to be further improved. More > Graphic Abstract

Weiru Wang¹, Yulong Cao^1,*, Yanxu Wang¹, Jiale You¹, Guangnan Zhang¹, Yu Xiao²

Energy Engineering, Vol.121, No.12, pp. 3801-3823, 2024, DOI:10.32604/ee.2024.055398 - 22 November 2024

Abstract To ensure frequency stability in power systems with high wind penetration, the doubly-fed induction generator (DFIG) is often used with the frequency fast response control (FFRC) to participate in frequency response. However, a certain output power suppression amount (OPSA) is generated during frequency support, resulting in the frequency modulation (FM) capability of DFIG not being fully utilised, and the system’s unbalanced power will be increased during speed recovery, resulting in a second frequency drop (SFD) in the system. Firstly, the frequency response characteristics of the power system with DFIG containing FFRC are analysed. Then, based… More >

Yuxuan Zhao^1,2,*, Bo Wang¹, Shu Wang¹, Wenjun Xu², Gang Ma²

Energy Engineering, Vol.121, No.12, pp. 3711-3733, 2024, DOI:10.32604/ee.2024.054032 - 22 November 2024

Abstract The output of photovoltaic power stations is significantly affected by environmental factors, leading to intermittent and fluctuating power generation. With the increasing frequency of extreme weather events due to global warming, photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions. The integration of these stations on a large scale into the power grid could potentially pose challenges to system stability. To address this issue, in this study, we propose a network architecture based on VMD-KELM for predicting the power output of photovoltaic power plants during severe weather… More >

Suneel Kumar Rath¹, Madhusmita Sahu¹, Shom Prasad Das², Junali Jasmine Jena³, Chitralekha Jena⁴, Baseem Khan^5,6,7,*, Ahmed Ali⁷, Pitshou Bokoro⁷

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1513-1536, 2024, DOI:10.32604/csse.2024.057067 - 22 November 2024

Abstract Redundancy, correlation, feature irrelevance, and missing samples are just a few problems that make it difficult to analyze software defect data. Additionally, it might be challenging to maintain an even distribution of data relating to both defective and non-defective software. The latter software class’s data are predominately present in the dataset in the majority of experimental situations. The objective of this review study is to demonstrate the effectiveness of combining ensemble learning and feature selection in improving the performance of defect classification. Besides the successful feature selection approach, a novel variant of the ensemble learning… More >

Tao Yan¹, Javed Rashid^2,3, Muhammad Shoaib Saleem^3,4, Sajjad Ahmad⁴, Muhammad Faheem^5,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2685-2708, 2024, DOI:10.32604/cmc.2024.058186 - 18 November 2024

Abstract Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and… More >

Trong Thua Huynh^*, Hoang Thanh Nguyen, Du Thang Phu

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2281-2298, 2024, DOI:10.32604/cmc.2024.057954 - 18 November 2024

Abstract In recent years, early detection and warning of fires have posed a significant challenge to environmental protection and human safety. Deep learning models such as Faster R-CNN (Faster Region based Convolutional Neural Network), YOLO (You Only Look Once), and their variants have demonstrated superiority in quickly detecting objects from images and videos, creating new opportunities to enhance automatic and efficient fire detection. The YOLO model, especially newer versions like YOLOv10, stands out for its fast processing capability, making it suitable for low-latency applications. However, when applied to real-world datasets, the accuracy of fire prediction is… More >

Nataliya Shakhovska^1,2,*, Mykola Medykovskyi¹, Oleksandr Gurbych^1,3, Mykhailo Mamchur^1,3, Mykhailo Melnyk¹

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3147-3163, 2024, DOI:10.32604/cmc.2024.056542 - 18 November 2024

Abstract The increasing adoption of solar photovoltaic systems necessitates accurate forecasting of solar energy production to enhance grid stability, reliability, and economic benefits. This study explores advanced machine learning (ML) and deep learning (DL) techniques for predicting solar energy generation, emphasizing the significant impact of meteorological data. A comprehensive dataset, encompassing detailed weather conditions and solar energy metrics, was collected and preprocessed to improve model accuracy. Various models were developed and trained with different preprocessing stages. Finally, three datasets were prepared. A novel hour-based prediction wrapper was introduced, utilizing external sunrise and sunset data to restrict… More >

Weixin Jiang^1,*, Zongze Li², Qing Yuan^3,*, Junhua Gong², Bo Yu⁴

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-3, 2024, DOI:10.32604/icces.2024.011266

Abstract High resolution flow field results are of great significance for exploring physical laws and guiding practical engineering practice. However, traditional activities based on experiments or direct numerical solutions to obtain high-resolution flow fields typically require a significant amount of computational time or resources. In response to this challenge, this study proposes an efficient and robust high-resolution flow field reconstruction method by embedding graph theory into neural networks, to adapt to low data volume situations. In the high resolution flow field reconstruction problem of an NS equation, the proposed model has a lower mean squared error More >

Md Mahfuzur Rahman^1,*, Md Solaiman Chowdhury², Mohammad Shorfuzzaman³, Lutful Karim⁴, Md Shafiullah⁵, Farag Azzedin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2501-2525, 2024, DOI:10.32604/cmes.2024.055065 - 31 October 2024

Abstract This article presents an innovative approach that leverages interpretable machine learning models and cloud computing to accelerate the detection of septic shock by analyzing electronic health data. Unlike traditional methods, which often lack transparency in decision-making, our approach focuses on early detection, offering a proactive strategy to mitigate the risks of sepsis. By integrating advanced machine learning algorithms with interpretability techniques, our method not only provides accurate predictions but also offers clear insights into the factors influencing the model’s decisions. Moreover, we introduce a preference-based matching algorithm to evaluate disease severity, enabling timely interventions guided… More >

Muhammad Azhar Ali Khan¹, Syed Sohail Akhtar^2,3,*, Abba A. Abubakar^2,4, Muhammad Asad¹, Khaled S. Al-Athel^2,5

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2325-2350, 2024, DOI:10.32604/cmes.2024.054741 - 31 October 2024

Abstract The fatigue life and reliability of wrought carbon steel castings produced with an optimized mold design are predicted using a finite element method integrated with reliability calculations. The optimization of the mold is carried out using MAGMASoft mainly based on porosity reduction as a response. After validating the initial mold design with experimental data, a spring flap, a common component of an automotive suspension system is designed and optimized followed by fatigue life prediction based on simulation using Fe-safe. By taking into consideration the variation in both stress and strength, the stress-strength model is used… More >