Article
Thermodynamics
Wenqiang Yang, Xinxin Zhu, Qinge Xiao, Zhile Yang
Summary: This paper proposes an improved version of the multi-objective marine predator algorithm (IMOMPA) for solving the optimization of multi-objective dynamic economic-grid fluctuation dispatch (MODEGD). The IMOMPA algorithm improves population diversity, convergence speed, and global search ability. Numerical experiments on benchmark functions and generation units demonstrate the superiority of the IMOMPA algorithm, and plug-in electric vehicles (PEVs) connected to the grid (V2G) can help mitigate grid fluctuations.
Article
Energy & Fuels
Xizheng Zhang, Zeyu Wang, Zhangyu Lu
Summary: The hybrid modified GSA-PSO scheme is proposed to optimize the load dispatch of the microgrid containing electric vehicles, which can significantly improve the safety and economy of the grid, reduce the total cost and load variance.
Article
Thermodynamics
Seyed Farhad Zandrazavi, Cindy Paola Guzman, Alejandra Tabares Pozos, Jairo Quiros-Tortos, John Fredy Franco
Summary: A stochastic multi objective optimization model is proposed for grid-connected unbalanced microgrids to minimize total operational cost and voltage deviation. The epsilon-constraint method and fuzzy satisfying approach are used to solve the multi-objective optimization problem, considering uncertainties through the roulette wheel mechanism for scenario generation.
Article
Computer Science, Information Systems
Wenqiang Yang, Xinxin Zhu, Fuquan Nie, Hongwei Jiao, Qinge Xiao, Zhile Yang
Summary: Dynamic economic dispatch plays a crucial role in power system operation and control. By integrating plug-in electric vehicles with the grid, fluctuations can be mitigated and the benefits of balancing peaks and filling valleys can be realized. This paper proposes a model that considers the day-ahead scheduling of power systems and the impact of PEVs, and introduces an improved chaos moth flame optimization algorithm (CMFO) to solve the problem.
Article
Thermodynamics
Yajun Wang, Jidong Wang, Man Cao, Xiangyu Kong, Bouchedjira Abderrahim, Long Yuan, Aris Vartosh
Summary: This paper proposes a power-pollution dynamic load dispatch method based on electric vehicles, aiming to simultaneously reduce fuel costs and pollution emissions by adding electric vehicles. A scenario-based probabilistic method is used to handle the uncertainty in wind farms, and the problem is considered dynamically using various factors. A novel multi-objective optimization algorithm based on harmony search is proposed, and a sorting model and fuzzy theory are employed for solution selection. The efficiency of the proposed model and method is demonstrated through different test systems.
Article
Thermodynamics
Nuh Erdogan, Sadik Kucuksari, Jimmy Murphy
Summary: This study proposes a multi-objective optimization model to determine the optimal charging infrastructure for a transition to plug-in electric vehicles (PEVs) at workplaces. The model considers all cost aspects of a workplace charging station and incorporates smart charging strategies and a charging behavior model. Through testing and sensitivity analysis, it is shown that the proposed model can achieve significant cost savings compared to single-objective optimal models and current charging practices.
Article
Remote Sensing
Olive Niyomubyeyi, Mozafar Veysipanah, Sam Sarwat, Petter Pilesjo, Ali Mansourian
Summary: This paper proposes an improved NSBBO algorithm for solving the multi-objective land-use allocation problem, and applies it to spatial analysis of the new urban planning area in Kigali, Rwanda. The results show that the algorithm can generate optimal land-use scenarios and support decision-making by urban planners and stakeholders.
GEO-SPATIAL INFORMATION SCIENCE
(2022)
Article
Computer Science, Information Systems
Hao Wang, Chaoli Sun, Guochen Zhang, Jonathan E. Fieldsend, Yaochu Jin
Summary: This study proposes a new multi-objective optimization method that converts multi-objective problems into bi-objective ones by using two performance indicators and non-dominated sorting to address engineering problems with multiple conflicting objectives. The method improves performance by balancing individual performance in different parts of the objective space and measuring diversity of each individual. Experimental results show competitiveness of the proposed method in solving problems with a large number of objectives.
INFORMATION SCIENCES
(2021)
Article
Thermodynamics
Niveditha Sivadanam, Nagu Bhookya, Sydulu Maheswarapu
Summary: The integration of non-synchronous distributed energy resources and energy storage systems into thermal grids affects the total synchronous inertia, which in turn impacts the frequency response of the electrical grid. The study proposes incorporating plug-in hybrid electric vehicles into load frequency control using a biogeography-based optimization algorithm. Results show that a virtual synchronous power-HVDC tie-link outperforms other tie-links in improving the frequency response, highlighting the role of EVs in enhancing dynamic performance.
CASE STUDIES IN THERMAL ENGINEERING
(2021)
Article
Thermodynamics
Hany M. Hasanien, Ibrahim Alsaleh, Marcos Tostado-Veliz, Miao Zhang, Ayoob Alateeq, Francisco Jurado, Abdullah Alassaf
Summary: This research introduces a novel technique, the Hybrid Particle Swarm Optimization and Sea Horse Optimization (PSOSHO) algorithm, for solving the optimal reactive power dispatch (ORPD) problem in electrical grids. Simulation studies verify its efficacy and real data on electric vehicles are incorporated for realistic analyses.
Article
Computer Science, Artificial Intelligence
Youjun An, Xiaohui Chen, Yinghe Li, Yaoyao Han, Ji Zhang, Haohao Shi
Summary: With the proposal of an improved non-dominated sorting biogeography-based optimization (INSBBO) algorithm, this paper aims to solve the (hybrid) multi objective flexible job-shop scheduling problem. By introducing the V-dominance principle, HVNS structure and ESS strategy, the algorithm's performance has been enhanced and shows better performance compared to other intelligent algorithms.
APPLIED SOFT COMPUTING
(2021)
Article
Energy & Fuels
Peng Zheng, Zheng Fang, Hai Li, Yajia Pan, Dabing Luo, Zutao Zhang
Summary: This study proposes a hybrid energy management approach to increase the use of renewable energy in electric chargers for electric vehicles. Wind turbines and solar power generating modules are used to provide energy, and optimization algorithms are employed to determine the optimal energy cost and grid dependence. The results show that the system can balance energy cost and grid dependence, with a majority of the energy coming from renewable sources.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Thermodynamics
Zhiguo Wang, Hongqian Wei, Gongwei Xiao, Youtong Zhang
Summary: This paper proposes a real-time energy management strategy for HEVs considering battery health. By predicting battery health status and SOC values and integrating energy optimization and online equivalent consumption minimization strategy, the proposed strategy aims to save energy and improve handling adaptiveness. Simulation and experimental tests have validated its superiority in terms of energy economy and maneuverability.
ENERGY CONVERSION AND MANAGEMENT
(2022)
Article
Engineering, Multidisciplinary
Sushmita Sharma, Nima Khodadadi, Apu Kumar Saha, Farhad Soleimanian Gharehchopogh, Seyedali Mirjalili
Summary: This paper presents a method to solve multi-objective optimization problems using the Butterfly Optimization Algorithm (BOA). The BOA is improved and extended to tackle multi-objective problems. Experimental results show that the new MONSBOA algorithm outperforms other algorithms in solving various types of problems.
JOURNAL OF BIONIC ENGINEERING
(2023)
Article
Energy & Fuels
Wang Ranran, Mu Jingying, Sun Zhihui, Wang Jinxing, Hu Anrui
Summary: This study establishes a regional electricity price model using the NSGA-II multi-objective optimization algorithm to reduce peak load through electricity consumption time while ensuring the interests of operators. The effectiveness of the algorithm is verified by an example in Shandong Province, demonstrating its practical significance in adjusting the charging load of electric vehicles without altering the space-time distribution of existing charging stations.
Article
Mathematics, Interdisciplinary Applications
Wenqiang Yang, Tingli Cheng, Yuanjun Guo, Zhile Yang, Wei Feng
Article
Automation & Control Systems
Shuqiang Wang, Xiangyu Wang, Yong Hu, Yanyan Shen, Zhile Yang, Min Gan, Baiying Lei
Summary: This study introduces a semisupervised multichannel generative adversarial network (MGAN) for grading diabetic retinopathy (DR), which generates subfundus images corresponding to the scattering DR features and reduces the dependence on labeled data to identify inconspicuous lesion features effectively in high-resolution fundus images. The proposed model outperforms other representative models in terms of accuracy, area under ROC curve (AUC), sensitivity, and specificity, showing potential for effective DR recognition.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Lidong Zhang, Tianyu Hu, Zhile Yang, Dongsheng Yang, Jianhua Zhang
Summary: The heat exchanger is an indispensable device in the energy and chemical industry and plays a vital role in optimizing design. This paper proposes a novel algorithm called EDOLSCA for the optimal design of heat exchangers, and its advantages have been validated in practical applications.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Thermodynamics
Xiaodong Zhu, Shihao Zhao, Zhile Yang, Ning Zhang, Xinzhi Xu
Summary: A novel parallel social learning particle swarm optimization method is proposed in this study to solve the power system scheduling problem with significant integration of renewable energy sources and plug-in electric vehicles. The results show that the method has superior performance in solving unit commitment problems considering the new energy sectors.
Article
Computer Science, Artificial Intelligence
Dongsheng Yang, Mingliang Wu, Di Li, Yunlang Xu, Xianyu Zhou, Zhile Yang
Summary: Flexible job shop scheduling problem (FJSP) has attracted research interests, and this paper proposes an improved dragonfly algorithm (DA) with a dynamic opposite learning (DOL) strategy to solve the complex large-scale FJSP. The effectiveness and innovation of the proposed algorithm are validated through comparison experiments, and the performance of the algorithm is verified on various test functions and real-world instances.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Energy & Fuels
Wenqiang Yang, Zhanlei Peng, Zhile Yang, Yuanjun Guo, Xu Chen
Summary: An enhanced exploratory whale optimization algorithm (EEWOA) is proposed to solve the complex Dynamic Economic Dispatch (DED) problem efficiently and effectively, by enhancing population diversity and improving variable repairing ability. EEWOA shows significant advantages over several state-of-the-art optimization algorithms on various benchmarks and DED cases.
Article
Energy & Fuels
Shihao Zhao, Kang Li, Zhile Yang, Xinzhi Xu, Ning Zhang
Summary: This paper proposes a novel power system rescheduling strategy to address the challenges posed by the uncertainty of renewable power generations. A particle swarm optimization algorithm based scheduling scheme is also introduced. Experimental results validate the effectiveness of the proposed framework and algorithm, achieving significant cost reduction and highlighting the importance of managing plug-in electric vehicle charging and discharging to alleviate the negative impact on grid stability caused by intermittent renewable power generations.
Article
Energy & Fuels
Junfeng Zhou, Yanhui Zhang, Yubo Zhang, Wen-Long Shang, Zhile Yang, Wei Feng
Summary: The paper proposes an adaptive differential evolution algorithm with the dynamic opposite learning strategy (DOLADE) to extract optimal parameters for PV cell models. The proposed algorithm shows improved accuracy, reliability, and computational efficiency in solving the problem.
Article
Engineering, Electrical & Electronic
Feixiang Zhou, Zheheng Jiang, Zhihua Liu, Fang Chen, Long Chen, Lei Tong, Zhile Yang, Haikuan Wang, Minrui Fei, Ling Li, Huiyu Zhou
Summary: This paper proposes a novel Hourglass network based model for pose estimation of mice, which incorporates effective modules of Structured Context Mixer and Cascaded Multi-level Supervision to enhance the robustness of the network. The proposed approach achieves accurate localization through the use of multi-level prediction information. Experimental results demonstrate its competitive performance against state-of-the-art approaches.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2022)
Article
Thermodynamics
Xiangfei Liu, Mifeng Ren, Zhile Yang, Gaowei Yan, Yuanjun Guo, Lan Cheng, Chengke Wu
Summary: This paper proposes a novel HVAC control system based on a multi-step predictive deep reinforcement learning algorithm. The system predicts the outdoor ambient temperature using the GC-LSTM algorithm and combines it with the DDPG algorithm to adjust the output power of the HVAC system based on the dynamic changing of electricity prices. Simulation results demonstrate the effectiveness of the system in saving costs while maintaining user comfort.
Article
Construction & Building Technology
Meng Yang, Chengke Wu, Yuanjun Guo, Rui Jiang, Feixiang Zhou, Jianlin Zhang, Zhile Yang
Summary: This paper presents a deep learning model called Spatial Temporal Relation Transformer (STR-Transformer) for automatically identifying risky behaviors in construction sites. By simultaneously extracting and fusing spatial and temporal features from video streams, the STR-Transformer enables more accurate and reliable safety surveillance, with potential to reduce accident rates and management costs.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Computer Science, Artificial Intelligence
Junjie Jiang, Zhile Yang, Chengke Wu, Yuanjun Guo, Wei Feng
Summary: Recent advancements in computer vision and augmented reality technology have the potential to enhance human-computer interaction, but their integration is not common in the industry. Efficient object detection models are crucial for accurate object localization in augmented reality devices, but the limited computing capabilities and memory usage of wearable AR devices pose challenges for deploying state-of-the-art detectors.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Lan Cheng, Zhao An, Yuanjun Guo, Mifeng Ren, Zhile Yang, Sean McLoone
Summary: This article proposes a multimodal few-shot learning method (MMFSL) for unbalanced data modeling of industrial bearings. MMFSL can handle time-series data and images, and evaluate the quality of the generated data. Through experiments, the MMFSL model can significantly improve fault detection accuracy and reduce false alarm rate. Moreover, the fault classification model accuracy is also improved compared to the original datasets.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Industrial
Lu Zhang, Yi Feng, Qinge Xiao, Yunlang Xu, Di Li, Dongsheng Yang, Zhile Yang
Summary: This paper investigates the difficulties of Dynamic Flexible Job Shop Scheduling (DFJSP) caused by the uncertainties and complexity in the production process due to customized requirements. A new DFJSP model, VPT-FJSP, is proposed and solved using Markov decision process (MDP) and reinforcement learning methods. The experimental results show that the proposed framework outperforms genetic algorithm and ant colony optimization in most cases, demonstrating its effectiveness and robustness.
JOURNAL OF MANUFACTURING SYSTEMS
(2023)
Review
Computer Science, Artificial Intelligence
Zhou Wu, Gan Luo, Zhile Yang, Yuanjun Guo, Kang Li, Yusheng Xue
Summary: This paper provides a comprehensive overview of wind energy forecasting models based on deep learning, and discusses future development directions. Wind energy forecasting plays an important role in improving wind energy utilization.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(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.
