Article
Engineering, Civil
Zhong-kai Feng, Tao Luo, Wen-jing Niu, Tao Yang, Wen-chuan Wang
Summary: This paper proposes a LSTM-based approximate dynamic programming (ADP) method to optimize the operation of complex hydropower reservoirs. The ADP method reduces redundant computations and improves execution efficiency by treating LSTM as the response surface model. Simulation results show that ADP effectively reduces execution time while maintaining solution quality in different scenarios.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Civil
Fuxin Chai, Feng Peng, Hongping Zhang, Wenbin Zang
Summary: This study proposes a stable improved dynamic programming (SIDP) method to solve the optimization problem of reservoir flood control operation. By combining relaxation method and a prediction method of schedulable storage states, SIDP overcomes the computational problem and convergence problem of dynamic programming. The case study demonstrates that SIDP achieves high accuracy and efficiency in reservoir flood control operation.
WATER RESOURCES MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Qing Wang
Summary: The paper introduces a knowledge-based approach that combines case-based reasoning and operational research methodologies to solve repetitive combinatorial optimization problems, utilizing past experience to improve problem-solving efficiency, especially in cases where traditional schemes cannot solve the problem due to its large dimension.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Water Resources
S. Jamshid Mousavi, Kumaraswamy Ponnambalam, Alcigeimes B. Celeste, Ximing Cai
Summary: The paper introduces a new implementation method named FP-2022, which significantly reduces solving time by simplifying constraints and decreasing decision variables. It incorporates new expressions to improve optimality for a nonlinear objective function. The method is proven to be highly efficient and optimal through the optimization problem of a dam in Brazil and a five-reservoir system in India.
ADVANCES IN WATER RESOURCES
(2022)
Article
Energy & Fuels
Shaokun He, Shenglian Guo, Jiabo Yin, Zhen Liao, He Li, Zhangjun Liu
Summary: The proposed framework in this study for joint and optimal impoundment operation for cascade reservoirs achieved significant improvements. Results show that the optimal policy can increase impoundment efficiency, hydropower generation, and reduce CO2 emissions while maintaining low flood control risk, compared to traditional operating rules.
Article
Economics
Xuekai Wang, Tao Tang, Shuai Su, Jiateng Yin, Ziyou Gao, Nan Lv
Summary: The paper proposes an integrated energy-efficient train operation method that jointly optimizes driving strategy and train timetable to minimize energy consumption in metro systems. By introducing calculation models, optimization models, and solution algorithms, the study successfully improves energy utilization efficiency and reduces energy consumption.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2021)
Article
Mathematics, Applied
K. Gajamannage, D. I. Jayathilake, Y. Park, E. M. Bollt
Summary: This paper discusses the limitations of traditional statistical methods for solving spatiotemporal dynamical systems and proposes artificial neural networks (ANNs) as a better approach. Specifically, recurrent neural networks (RNNs) are introduced as a type of ANN capable of processing variable-length input sequences, making them applicable for a wide range of problems in spatiotemporal dynamical systems. The paper analyzes the performance of RNNs in three tasks, including repairing erroneous Lorenz equations, repairing damaged collective motion trajectories, and forecasting streamflow time series with spikes, demonstrating the broad applicability of RNNs in reconstructing and forecasting the dynamics of dynamical systems.
Article
Computer Science, Interdisciplinary Applications
Lei Wang, Puying Zhao, Jun Shao
Summary: This article introduces three different semi-parametric estimation methods to estimate distribution functions and quantiles of a response variable. An instrumental covariate is used to address the identifiability problem, and dimension reduction technique is employed to improve efficiency. The proposed estimators have been shown to be consistent and asymptotically normal.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2021)
Article
Energy & Fuels
Suiling Wang, Zhiqiang Jiang, Yi Liu
Summary: In this paper, an improved dynamic programming method is proposed for flood control scheduling of reservoirs, which can effectively solve the problem of failing to obtain the optimal solution using conventional scheduling methods, and significantly improves the calculation efficiency.
Article
Mathematics, Applied
Alec J. Linot, Michael D. Graham
Summary: In this study, we propose a data-driven reduced-order modeling method for chaotic dynamics, which involves finding the coordinate representation of the manifold and describing the dynamics using a system of ordinary differential equations (ODEs) in this coordinate system. We apply this method to a specific system and find that dimension reduction improves performance compared to predictions in the ambient space. Furthermore, we demonstrate that the low-dimensional model is capable of accurately recreating the true dynamics using widely spaced data.
Article
Engineering, Civil
Yufei Ma, Ping-an Zhong, Bin Xu, Feilin Zhu, Jieyu Li, Han Wang, Qingwen Lu
Summary: A novel parallel dynamic programming algorithm based on Spark via cloud computing is proposed for optimal operation of reservoir system. The efficiency of cloud-based PDPoS is influenced by factors like the number of CPU cores, with high stability and good scalability. Cloud computing has rich resources and good portability of online operations, providing an attractive alternative for large-scale reservoir system operation.
WATER RESOURCES MANAGEMENT
(2021)
Article
Engineering, Civil
Wenting Jin, Yimin Wang, Jianxia Chang, Xuebin Wang, Chen Niu, Yu Wang, Shaoming Peng
Summary: This paper proposes a novel framework to balance multiple objectives in reservoir operation and optimize the model to enhance the satisfaction of multiple requirements.
JOURNAL OF HYDROLOGY
(2021)
Article
Mathematics, Applied
Martin Hutzenthaler, Arnulf Jentzen, Thomas Kruse, Tuan Anh Nguyen
Summary: Backward stochastic differential equations (BSDEs) are extensively studied in stochastic analysis and computational stochastics. Nonlinear and high-dimensional BSDEs are common in real applications, but exact solutions are rarely attainable. Therefore, it is crucial to develop and analyze numerical approximation methods for solving these complex problems.
JOURNAL OF NUMERICAL MATHEMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Ramtin Moeini, Kamran Nasiri
Summary: The study proposed a method for determining the operating policies of a dam reservoir based on the genetic programming model, with two approaches for selecting input variables using hybrid and GP methods. The results showed acceptable performance, with SI index and water deficit achieving good values.
Article
Energy & Fuels
Mahdi Ghadiri, Azam Marjani, Reza Daneshfar, Saeed Shirazian
Summary: This paper introduces a reduced-order model called SOD-DEIM for flow simulations in two-phase reservoirs, and compares it with the POD-DEIM model. The results show that, despite lower computational costs, SOD-DEIM outperforms POD-DEIM in terms of the accuracy of simulation results.
JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Zhong-kai Feng, Wen-jing Niu
Summary: This study proposes a hybrid river flow forecasting method by integrating the novel cooperation search algorithm (CSA) into the learning process of artificial neural network (ANN). The experimental results show that the hybrid method based on ANN and CSA outperforms control models and yields superior forecasting results in different scenarios. The presented method demonstrates improvements in the efficiency and correlation values of the standard ANN method, indicating that the performance of artificial intelligence models in river flow time series forecasting can be effectively improved by metaheuristic algorithms.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Engineering, Civil
Wen-jing Niu, Zhong-kai Feng, Shuai Liu, Yu-bin Chen, Yin-shan Xu, Jun Zhang
Summary: The study presents an improved HGWO method to effectively solve the optimization problem of hydropower reservoir operation, producing better results. Through experimental validation and application to a real-world hydropower system, the HGWO method outperforms other control methods in terms of statistical indicators.
WATER RESOURCES MANAGEMENT
(2021)
Article
Construction & Building Technology
Wen-jing Niu, Zhong-kai Feng
Summary: Accurate runoff forecasting is crucial for ensuring sustainable utilization and management of water resources. Research indicates that support vector machine, Gaussian process regression, and extreme learning machine outperform artificial neural network and adaptive neural based fuzzy inference system in streamflow prediction, emphasizing the importance of selecting appropriate forecasting models based on reservoir characteristics.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Computer Science, Artificial Intelligence
Zhong-kai Feng, Jie-feng Duan, Wen-jing Niu, Zhi-qiang Jiang, Yi Liu
Summary: This study proposes an enhanced sine cosine algorithm (ESCA) to improve the performance of the Sine Cosine Algorithm (SCA) in multivariable optimization problems. ESCA incorporates several modified strategies to enhance its search range, global exploration, population diversity, and solution quality. Experimental results demonstrate that ESCA outperforms traditional methods in terms of solution efficiency and convergence rate for multivariable parameter optimization problems. The feasibility of ESCA in practical applications is further confirmed through engineering optimization problems, where ESCA produces high-quality solutions with better objective values.
APPLIED SOFT COMPUTING
(2022)
Editorial Material
Environmental Sciences
Shiping Wen, Zhong-kai Feng, Tingwen Huang, Nian Zhang
ENVIRONMENTAL RESEARCH LETTERS
(2022)
Article
Engineering, Civil
Zhong-kai Feng, Peng-fei Shi, Tao Yang, Wen-jing Niu, Jian-zhong Zhou, Chun-tian Cheng
Summary: This study develops a hydrological forecasting model based on parallel cooperation search algorithm and extreme learning machine. The results demonstrate that the proposed model outperforms traditional artificial intelligence models in predicting nonlinear streamflow time series.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Civil
Zhong-kai Feng, Wen-jing Niu, Peng-fei Shi, Tao Yang
Summary: Reservoir is an important engineering measure for efficient utilization of water resources, and accurate simulation and prediction of discharge data is crucial. This paper proposes a hybrid simulation method using cooperative search algorithm and adaptive neuro-fuzzy inference system, which shows better performance in simulating reservoir discharge data.
WATER RESOURCES MANAGEMENT
(2022)
Editorial Material
Engineering, Industrial
Zhongkai Feng, Wenjing Niu, Chuntian Cheng, Jianzhong Zhou, Tao Yang
Summary: Wind and solar powers will gradually become dominant energies towards carbon neutrality. Large-scale renewable energies, with strong stochasticity, high volatility, and unadjustable features, have significant impacts on the safe operation of power systems. Therefore, an advanced hydropower energy system serving multiple energies is needed to respond to volatility.
FRONTIERS OF ENGINEERING MANAGEMENT
(2022)
Article
Engineering, Civil
Zhong-kai Feng, Wen-jing Niu, Xin-yu Wan, Bin Xu, Fei-lin Zhu, Juan Chen
Summary: This study proposes a hybrid model combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and twin support vector machine (TSVM) for accurate hydrologic forecasting. Experimental results show that the hybrid model significantly outperforms conventional models in prediction accuracy.
JOURNAL OF HYDROLOGY
(2022)
Article
Thermodynamics
Zhong-kai Feng, Qing-qing Huang, Wen-jing Niu, Tao Yang, Jia-yang Wang, Shi-ping Wen
Summary: This research proposes an effective forecasting method for solar output using CEEMDAN, GRU, and CSA, which can yield accurate forecasting results by identifying suitable dependencies and network structures through data decomposition technique and machine learning.
Editorial Material
Environmental Sciences
Wenchuan Wang, Zhongkai Feng, Mingwei Ma
Editorial Material
Environmental Sciences
Zhong-kai Feng, Shi-ping Wen, Wen-jing Niu
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2023)
Editorial Material
Engineering, Civil
Wen-jing Niu, Zhong-kai Feng, Yin-shan Xu, Bao-fei Feng, Yao-wu Min
JOURNAL OF HYDROLOGIC ENGINEERING
(2023)
Article
Environmental Sciences
Wenchuan Wang, Weican Tian, Kwokwing Chau, Hongfei Zang, Mingwei Ma, Zhongkai Feng, Dongmei Xu
Summary: This paper proposes an improved bald eagle search algorithm (CABES) combined with epsilon-constraint method (epsilon-CABES) to tackle the complex reservoir flood control operation problem. Through simulations and comparisons with other algorithms, the superior performance of the CABES algorithm is verified. The results of the tests on single and multi-reservoir systems show that the epsilon-CABES method outperforms other methods in flood control scheduling.
Article
Computer Science, Artificial Intelligence
Ziyu Sheng, Shiping Wen, Zhong-kai Feng, Jiaqi Gong, Kaibo Shi, Zhenyuan Guo, Yin Yang, Tingwen Huang
Summary: Runoff forecasting, an important branch of time series forecasting, plays a vital role in rational water resource utilization, economic development, and ecological management of river basins. The data-driven model has become the mainstream method for runoff forecasting with the advancement in computing power. This survey explores various neural network models, including convolutional neural network (CNN), recurrent neural network (RNN), and Transformer, for runoff forecasting. The advantages, limitations, and future improvement directions of these models are discussed, focusing on accuracy, robustness, and interpretability.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE
(2023)
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.
