Article
Green & Sustainable Science & Technology
Kishan Bhushan Sahay, Mohammed A. S. Abourehab, Abolfazl Mehbodniya, Julian L. Webber, Ravi Kumar, Ulaganathan Sakthi
Summary: This paper proposes a multi-objective optimization method for reducing energy consumption and time of EV charging stations, integrating energy management system and meta-heuristics algorithm to achieve effective placement in a microgrid, and optimal energy savings through clustering.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Computer Science, Information Systems
Ashwani Kumar, Ravinder Kumar, Ashutosh Aggarwal
Summary: This paper presents a solution to the problem of charging convenience for electric vehicles (EVs). By analyzing factors such as the availability and convenience of charging stations, an improved distributed system is proposed to plan energy efficient charging routes. The system also introduces an agile charging slot reservation approach to minimize energy consumption, waiting time, and charging expenditure.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Theory & Methods
Haixia Zhang, Qingxiu Peng
Summary: Accurate semantic segmentation of agricultural products is essential in computer vision. Utilizing a hybrid clustering method of particle swarm and K-means in the YCbCr color space can effectively mitigate the effects of low illumination and shadow, while achieving a balance between global solution search ability and convergence speed. The proposed method demonstrates better segmentation stability and accuracy compared to traditional clustering methods, and is suitable for segmenting agricultural product images in various complex environments.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2022)
Review
Energy & Fuels
Sigma Ray, Kumari Kasturi, Samarjit Patnaik, Manas Ranjan Nayak
Summary: Vehicles worldwide are transitioning to electric power as a response to climate change and pollution reduction. To support this transition, more electric vehicle charging stations (EVCS) need to be established for the public. However, the uncoordinated growth of electric vehicles and charging stations will have significant technical, economic, and environmental consequences, necessitating the coordination and scheduling of charging/discharging processes. This paper explores different approaches for optimal EVCS placement, evaluates charging procedures, control management, and coordination with distribution networks.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Engineering, Electrical & Electronic
Brenda Rojas-Delgado, Chisom Ekweoba, George Lavidas, Irina Temiz
Summary: This paper proposes and analyzes a genetic algorithm based permutation control logic applied to the aggregator of an offshore multi-source park. The results show that implementing this approach can positively influence energy losses and capacity factors. The research also developed a control system capable of tracking the set-point imposed by the demand curve for each source individually and unveiled pure offshore renewable sources as potential storage-less flexibility service providers.
Article
Materials Science, Textiles
Yi Sun, Gui Liu, Dongming Zheng, Haochen Zou, Zhenrui Liu, Jinkang Liu, Zhaoqun Du
Summary: This study proposes a new clustering algorithm for evaluating fabric tactile comfort and compares it with other algorithms using the QIHES method to determine mechanical properties and extract feature indexes. The results show that the SAPSO-K algorithm is the most effective and reliable method for accurately predicting rating values and evaluating tactile comfort.
JOURNAL OF THE TEXTILE INSTITUTE
(2022)
Article
Computer Science, Artificial Intelligence
Haowei Zhang, Junwei Xie, Binfeng Zong
Summary: The proposed bi-objective hybrid particle swarm optimization algorithm aims to optimize the search and track functions simultaneously in D-MIMO-R systems. By balancing exploitation and exploration abilities, designing a heuristic mapping scheme to handle constraints, and using a distance-based crowding function to preserve swarm diversity, the algorithm proves to be effective and efficient compared to state-of-the-art algorithms.
APPLIED SOFT COMPUTING
(2021)
Article
Engineering, Electrical & Electronic
Sanghamitra Mishra, Arijit Mondal, Samrat Mondal
Summary: The problem of charge scheduling of Electric Vehicles (EVs) at charging stations remains a significant challenge due to long charging time and insufficient charging infrastructure. In this study, we propose an efficient EV charge scheduling plan for a charging station equipped with adaptable charging ports, aiming to improve performance and maximize profit and customer satisfaction.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Ioan-Daniel Borlea, Radu-Emil Precup, Alexandra-Bianca Borlea, Daniel Iercan
Summary: This paper introduces the novel Unified Form (UF) clustering algorithm and the Partitional Implementation of Unified Form (PIUF) algorithm, aiming to address the challenges of processing large datasets and sequential data processing. These algorithms are implemented and validated in the BigTim platform and can be applied to other data processing platforms.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Vikas Palakonda, Jae-Mo Kang, Heechul Jung
Summary: An ensemble approach combining mating and environmental selection operators of different MOEAs using AdaBoost and K-means clustering algorithms is proposed to enhance the performance of MOEAs on MaOPs.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Green & Sustainable Science & Technology
Ajit Kumar Mohanty, Perli Suresh Babu, Surender Reddy Salkuti
Summary: This paper proposes a multi-objective optimization method for the simultaneous optimal allocation of FCEs, DGs, and SCs. The proposed method outperforms other existing algorithms in terms of cost reduction, voltage stability, and meeting transportation requirements.
Article
Mathematics, Interdisciplinary Applications
Chunqiong Wu, Bingwen Yan, Rongrui Yu, Baoqin Yu, Xiukao Zhou, Yanliang Yu, Na Chen
Summary: This paper extensively studies the parallel k-means algorithm, which speeds up the efficiency of the algorithm by parallelizing the distance calculation and data object clustering processes, showing efficient and stable service provision with good convergence.
Article
Construction & Building Technology
Yongyi Huang, Hasan Masrur, Molla Shahadat Hossain Lipu, Harun Or Rashid Howlader, Mahmoud M. Gamil, Akito Nakadomari, Paras Mandal, Tomonobu Senjyu
Summary: In order to enhance the efficiency and stability of renewable energy sources and energy security in microgrids, this paper proposes an optimal campus microgrid design that includes EV charging load prediction and a constant power support strategy from the main grid. The paper presents a detailed prediction method and effective solutions to address the problem of load variation caused by changes in the number of EVs connected to the microgrid. Simulation results show that the microgrid system can operate economically and stably with EV integration, reducing peak-to-valley value and CO2 emissions and increasing the income of EV users.
SUSTAINABLE CITIES AND SOCIETY
(2023)
Article
Computer Science, Hardware & Architecture
Ramzi A. A. Haraty, Ali Assaf
Summary: Clustering is the process of dividing objects into classes based on their similarities. Traditional centralized algorithms cannot handle distributed objects, but distributed clustering algorithms can extract a classification model from objects distributed across different locations. With the increasing storage of data in various sites and the large amount of data on the web, distributed clustering is becoming a prominent field. Despite the challenges such as limited bandwidth and data transfer issues, the DG-means algorithm shows superior performance compared to other algorithms when evaluated on different metrics like runtime, stability, and accuracy.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Automation & Control Systems
Yang Yang, Qianfeng Liao, Jiang Wang, Yuan Wang
Summary: This paper proposes an improved multi-objective particle swarm optimizer, called MOPSO-SMK, to solve multi-modal multi-objective optimization problems where the same PF may correspond to multiple different PSs. By introducing short-term memory and K-means clustering, the algorithm achieves better performance compared to other three multi-objective optimization algorithms in terms of four indexes.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Thermodynamics
Yong Cheng, Fukai Song, Lei Fu, Saishuai Dai, Zhiming Yuan, Atilla Incecik
Summary: This paper investigates the accessibility of wave energy absorption by a dual-pontoon floating breakwater integrated with hybrid-type wave energy converters (WECs) and proposes a hydraulic-pneumatic complementary energy extraction method. The performance of the system is validated through experiments and comparative analysis.
Article
Thermodynamics
Jing Gao, Chao Wang, Zhanwu Wang, Jin Lin, Runkai Zhang, Xin Wu, Guangyin Xu, Zhenfeng Wang
Summary: This study aims to establish a new integrated method for biomass cogeneration project site selection, with a focus on the application of the model in Henan Province. By integrating Geographic Information System and Multiple Criterion Decision Making methods, the study conducts site selection in two stages, providing a theoretical reference for the construction of biomass cogeneration projects.
Article
Thermodynamics
Mert Temiz, Ibrahim Dincer
Summary: The current study presents a hybrid small modular nuclear reactor and solar-based system for sustainable communities, integrating floating and bifacial photovoltaic arrays with a small modular reactor. The system efficiently generates power, hydrogen, ammonia, freshwater, and heat for residential, agricultural, and aquaculture facilities. Thermodynamic analysis shows high energy and exergy efficiencies, as well as large-scale ammonia production meeting the needs of metropolitan areas. The hybridization of nuclear and solar technologies offers advantages of reliability, environmental friendliness, and cost efficiency compared to renewable-alone and fossil-based systems.
Editorial Material
Thermodynamics
Wojciech Stanek, Wojciech Adamczyk
Article
Thermodynamics
Desheng Xu, Yanfeng Li, Tianmei Du, Hua Zhong, Youbo Huang, Lei Li, Xiangling Duanmu
Summary: This study investigates the optimization of hybrid mechanical-natural ventilation for smoke control in complex metro stations. The results show that atrium fires are more significantly impacted by outdoor temperature variations compared to concourse/platform fires. The gathered high-temperature smoke inside the atrium can reach up to 900 K under a 5 MW train fire energy release. The findings provide crucial engineering insights into integrating weather data and adaptable ventilation protocols for smoke prevention/mitigation.
Article
Thermodynamics
Da Guo, Heping Xie, Mingzhong Gao, Jianan Li, Zhiqiang He, Ling Chen, Cong Li, Le Zhao, Dingming Wang, Yiwei Zhang, Xin Fang, Guikang Liu, Zhongya Zhou, Lin Dai
Summary: This study proposes a new in-situ pressure-preserved coring tool and elaborates its pressure-preserving mechanism. The experimental and field test results demonstrate that this tool has a high pressure-preservation capability and can maintain a stable pressure in deep wells. This study provides a theoretical framework and design standards for the development of similar technologies.
Article
Thermodynamics
Aolin Lai, Qunwei Wang
Summary: This study assesses the impact of China's de-capacity policy on renewable energy development efficiency (REDE) using the Global-MSBM model and the difference-in-differences method. The findings indicate that the policy significantly enhances REDE, promoting technological advancements and marketization. Moreover, regions with stricter environmental regulations experience a higher impact.
Article
Thermodynamics
Mostafa Ghasemi, Hegazy Rezk
Summary: This study utilizes fuzzy modeling and optimization to enhance the performance of microbial fuel cells (MFCs). By simulating and analyzing experimental data sets, the ideal parameter values for increasing power density, COD elimination, and coulombic efficiency were determined. The results demonstrate that the fuzzy model and optimization methods can significantly improve the performance of MFCs.
Article
Thermodynamics
Zhang Ruan, Lianzhong Huang, Kai Wang, Ranqi Ma, Zhongyi Wang, Rui Zhang, Haoyang Zhao, Cong Wang
Summary: This paper proposes a grey box model for fuel consumption prediction of wing-diesel hybrid vessels based on feature construction. By using both parallel and series grey box modeling methods and six machine learning algorithms, twelve combinations of prediction models are established. A feature construction method based on the aerodynamic performance of the wing and the energy relationship of the hybrid system is introduced. The best combination is obtained by considering the root mean square error, and it shows improved accuracy compared to the white box model. The proposed grey box model can accurately predict the daily fuel consumption of wing-diesel hybrid vessels, contributing to operational optimization and the greenization and decarbonization of the shipping industry.
Article
Thermodynamics
Huayi Chang, Nico Heerink, Junbiao Zhang, Ke He
Summary: This study examines the interaction between off-farm employment decisions between couples and household clean energy consumption in rural China, and finds that two-paycheck households are more likely to consume clean energy. The off-farm employment of women is a key factor driving household clean energy consumption to a higher level, with wage-employed wives having a stronger influence on these decisions than self-employed ones.
Article
Thermodynamics
Hanguan Wen, Xiufeng Liu, Ming Yang, Bo Lei, Xu Cheng, Zhe Chen
Summary: Demand-side management is crucial to smart energy systems. This paper proposes a data-driven approach to understand the relationship between energy consumption patterns and household characteristics for better DSM services. The proposed method uses a clustering algorithm to generate optimal customer groups for DSM and a deep learning model for training. The model can predict the possibility of DSM membership for a given household. The results demonstrate the usefulness of weekly energy consumption data and household socio-demographic information for distinguishing consumer groups and the potential for targeted DSM strategies.
Article
Thermodynamics
Xinglan Hou, Xiuping Zhong, Shuaishuai Nie, Yafei Wang, Guigang Tu, Yingrui Ma, Kunyan Liu, Chen Chen
Summary: This study explores the feasibility of utilizing a multi-level horizontal branch well heat recovery system in the Qiabuqia geothermal field. The research systematically investigates the effects of various engineering parameters on production temperature, establishes mathematical models to describe their relationships, and evaluates the economic viability of the system. The findings demonstrate the significant economic feasibility of the multi-level branch well system.
Article
Thermodynamics
Longxin Zhang, Songtao Wang, Site Hu
Summary: This investigation reveals the influence of tip leakage flow on the modern transonic rotor and finds that the increase of tip clearance size leads to a decline in rotor performance. However, an optimal tip clearance size can extend the rotor's stall margin.
Article
Thermodynamics
Kristian Gjoka, Behzad Rismanchi, Robert H. Crawford
Summary: This paper proposes a framework for assessing the performance of 5GDHC systems and demonstrates it through a case study in a university campus in Melbourne, Australia. The results show that 5GDHC systems are a cost-effective and environmentally viable solution in mild climates, and their successful implementation in Australia can create new market opportunities and potential adoption in other countries with similar climatic conditions.
Article
Thermodynamics
Jianwei Li, Guotai Wang, Panpan Yang, Yongshuang Wen, Leian Zhang, Rujun Song, Chengwei Hou
Summary: This study proposes an orientation-adaptive electromagnetic energy harvester by introducing a rotatable bluff body, which allows for self-regulation to cater for changing wind flow direction. Experimental results show that the output power of the energy harvester can be greatly enhanced with increased rotatory inertia of the rotating bluff body, providing a promising solution for harnessing wind-induced vibration energy.