Review
Chemistry, Analytical
Nemesio Fava Sopelsa Neto, Stefano Frizzo Stefenon, Luiz Henrique Meyer, Raul Garcia Ovejero, Valderi Reis Quietinho Leithardt
Summary: This paper proposes a hybrid model for monitoring the electrical power grid by improving existing models using wavelet transform. The results show that using wavelet transform can significantly improve model performance, especially the wavelet ANFIS model.
Article
Computer Science, Artificial Intelligence
Jin Xiao, Chunyan Li, Bo Liu, Jing Huang, Ling Xie
Summary: This study introduces the group method of data handling (GMDH) technique and proposes a GMDH-based selective deep ensemble (GSDE) model for the prediction of wind turbine blade icing fault. The experiments show that the proposed model outperforms existing ensemble models and single deep learning models in terms of prediction performance.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Sheng-Xiang Lv, Lu Peng, Huanling Hu, Lin Wang
Summary: This study develops a selective machine learning ensemble model (SMLE) that utilizes a novel soft selection algorithm to improve prediction accuracy. Experimental results show that the proposed model outperforms individual forecasts, advanced techniques, and ensemble strategies in terms of accuracy and reliability.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Zhengjin Zhang, Qilin Wu, Yong Zhang, Li Liu
Summary: In recent years, recommendation systems have become crucial in major streaming video platforms. This article proposes the Hybrid AdaBoost Ensemble Method, which uses fuzzy clustering and neural network training to improve scoring prediction accuracy, and introduces the AdaBoost integration method to enhance the stability of the model.
PEERJ COMPUTER SCIENCE
(2023)
Article
Agriculture, Multidisciplinary
Jamshid Piri, Bahareh Pirzadeh, Behrooz Keshtegar, Mohammad Givehchi
Summary: By utilizing a hybrid multi-objective algorithm called Group Method of Data Handling (GMDH), this study has developed a new model for predicting wastewater discharge. The GMDH-based neural network identified hidden relationships and accurately predicted output variables, with the hybrid model of genetic algorithm with GMDH showing the best accuracy. The models demonstrated high predictive accuracy at the Zabol and Zahedan stations.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Ergonomics
Haining Meng, Xinyu Tong, Yi Zheng, Guo Xie, Wenjiang Ji, Xinhong Hei
Summary: This research proposes an ensemble learning strategy for railway accident prediction, including an improved KNN data imputation algorithm and AdaBoost-Bagging method. Experimental results show that this method has smaller prediction error and faster inference time in predicting railway accidents, and can mine important accident features.
ACCIDENT ANALYSIS AND PREVENTION
(2022)
Article
Computer Science, Artificial Intelligence
Xufeng Niu, Wenping Ma
Summary: This paper proposes a new selective quantum ensemble learning model (SELA) based on improved AdaBoost and local sample information. SELA combines information entropy and random subspace to preserve important features of the classification task. It selects a base classifier that balances accuracy and diversity among a group of base classifiers generated based on local AdaBoost. The quantum genetic algorithm is used to search optimal weights for base learners in the label prediction process.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Serkan Aras
Summary: Research indicates that using higher model orders improves the accuracy of volatility forecasts for hybrid GARCH models, while stacking ensemble with LASSO produces superior forecasts compared to other hybrid models.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Operations Research & Management Science
Zengyuan Wu, Lizheng Jing, Bei Wu, Lingmin Jin
Summary: This paper proposes an improved prediction model that addresses the challenges of predicting customer churn in the e-commerce industry using traditional models. Experimental results demonstrate that the model can achieve more accurate customer churn prediction and better overall stability.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Vangala Sarveswararao, Vadlamani Ravi, Yelleti Vivek
Summary: This paper proposes a model for chaos and forecast in the ATM cash withdrawal time series of a large Indian commercial bank using deep learning and hybrid DL methods. The influence of the day-of-the-week variable on the results is also considered. LSTM performs the best in forecasting when considering the day-of-the-week variable.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Genetics & Heredity
Shahid Mohammad Ganie, Pijush Kanti Dutta Pramanik, Majid Bashir Malik, Saurav Mallik, Hong Qin
Summary: This study successfully predicted diabetes using machine learning algorithms with a high accuracy rate, and gradient boosting algorithm achieved the best performance among all classifiers. The results demonstrate the applicability of the suggested model for other diseases with similar predicate indications.
FRONTIERS IN GENETICS
(2023)
Article
Computer Science, Hardware & Architecture
Hongle Du, Yan Zhang
Summary: The paper introduces a two-level selective ensemble learning algorithm for imbalanced datasets, which shows improvement in classification performance in experiments, especially for imbalanced datasets.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Green & Sustainable Science & Technology
Huaiping Jin, Lixian Shi, Xiangguang Chen, Bin Qian, Biao Yang, Huaikang Jin
Summary: A novel probabilistic wind power forecasting method based on selective ensemble of finite mixture Gaussian process regression models (SEFMGPR) is proposed in this study, which enhances prediction accuracy by constructing diverse GPR models, integrating FMGPR models, and selecting highly influential models using genetic algorithm. Additionally, an incremental adaptation mechanism is employed to alleviate performance degradation. Application results demonstrate that SEFMGPR outperforms traditional global and ensemble wind power prediction methods in handling time-varying changes and maintaining high prediction accuracy.
Article
Green & Sustainable Science & Technology
Marcelo Azevedo Costa, Ramiro Ruiz-Cardenas, Leandro Brioschi Mineti, Marcos Oliveira Prates
Summary: This paper introduces a novel analog-based methodology for multi-step time series forecasting, called dynamic time scan forecasting (DTSF), which combines similarity functions with goodness-of-fit statistics to predict future multi-step data by identifying similar patterns throughout the time series. An ensemble version of the method (eDTSF) achieves competitive results in wind speed time series forecasting, even in situations of high variability, compared to eleven selected concurrent forecasting methods.
Article
Agronomy
Debnath Bhattacharyya, Eali Stephen Neal Joshua, N. Thirupathi Rao, Tai-hoon Kim
Summary: ICT breakthroughs have played a vital role in global social and economic development. Rural Indians heavily rely on agriculture for income, and the increasing population demands modernized farming practices. ICT is essential for educating farmers on environmentally friendly techniques, enhancing food production by solving various challenges. This research focuses on predicting soil moisture and categorizing sugarcane output, aiming to assist farmers and agricultural authorities in boosting production.
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.