检索结果-内蒙古大学图书馆

Optimal allocation of industrial park multi-energy complementary system based on typical scenarios: Case study of Shenzhen

引用

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 88卷 830-840页

作者： Liu, Fangtong Zhong, Jiaqi Wu, Man Liu, Xiaoyang Wang, Chaolang Ke, Yiming Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai 519070 Peoples R China China Nucl Energy Technol Corp Ltd Beijing 100080 Peoples R China Jinan Univ Int Energy Coll Zhuhai 519070 Peoples R China Jinan Univ Energy & Elect Res Ctr Zhuhai 519070 Peoples R China

The multi-energy complementary system (MECS) is a new mode that converts renewables into electricity and is usually equipped with hydrogen storage. It realizes flexible conversion of electric and hydrogen energy, achieving high efficiency and low carbon. However, how to optimize capacity of each equipment based on operation strategies and objectives becomes the most important issue. Thus, this study focuses on MECS optimal allocation and manages to work out a scientific framework. Firstly, the Density-Based Spatial Clustering of Applications with Noise algorithm and the Calinski Harabasz Index are firstly integrated to improve K-means clustering algorithm and identify typical scenarios. It can efficiently deal with long-sequence scenario reduction under the uncertainty of generation and load demand. Then, different from conventional objective-determining processes, indicators of the net present value, the carbon emission reduction, the power curtailment rate are taken into account to reflect comprehensive benefits from the life cycle of the MECS. Subsequently, for the difficulty of mixed integer fraction optimization, the-constraint method is introduced in model solving. Finally, a case study of Shenzhen is carried out for verification. The proposed models can achieve optimal allocation with different preference, which can provide theoretical and technical reference for MECS development.

关键词： Capacity allocation optimization Wind-PV-Hydrogen complementary system Typical scenario identification multi-objective optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel decomposition-ensemble forecasting system for dynamic dispatching of smart grid with sub-model selection and intelligent optimization

引用

EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期201卷 1页

作者： Wang, Jianzhou Zhang, Lifang Liu, Zhenkun Niu, Xinsong Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Short-Term Electricity Load Forecasting (STLF) has drawn increasing attention due to its considerable function on improving the dispatch and management of power system. The path of load forecasting has been widely explored in the past studies. However, most forecasting methods ignore the importance of sub-model selection and interval forecasting, causing poor forecasting performance. In this paper, a hybrid ensemble forecasting scheme, constructed by sub-model selection, data decomposition, multi-objective optimization, point prediction, and interval prediction, is proposed to enrich the current load forecasting system. In the load forecasting system design, the original load data is adaptively decomposed into some sub-series by using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). The most proper sub-model for decomposed series is selected from nine widely-used forecasting methods based on sub-model selection. The forecasting results of each sub-series are ensembled by the multi-objective Grasshopper optimization algorithm (MOGOA) to estimate the final point forecasting values. By integrating point forecasting errors and distribution fitting strategy, the interval forecasting results are further estimated, which investigates the effectiveness of the proposed system. Three numerical experiments are carried out based on the real load data obtained from three observation sites. The experimental results indicate that the hybrid ensemble system can provide better point and interval forecasting performance with the average Mean Absolute Percentage Error (MAPE) values of 0.3275%, 0.4419%, and 0.7024% and the mean Coverage Width-based Criteria (CWC) values (alpha = 0.05) of 0.0668, 0.1335, and 0.3178 from one-step to three-step forecasting, respectively.

关键词： Short-term load forecasting Sub-model selection Hybrid ensemble forecasting Data preprocessing multi-objective optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

multi-objective topology optimization and flow characteristics study of the microfluidic reactor

引用

REACTION KINETICS MECHANISMS AND CATALYSIS 2022年第5期135卷 2475-2501页

作者： Wang, Jiahao Wang, Yue Ma, Lie Liu, Xiaomin Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Peoples R China Guangdong Midea Air Conditioning Equipment Co Ltd Foshan 528311 Guangdong Peoples R China

An improved topology optimization method is proposed to optimize the performance and structure of the packed bed microfluidic reactor (PBMR). Dimensionless general scaling factor (GSF) is established to fully reflect the multi-physical properties of first-order isothermal catalytic reaction. Reaction performance and the flow loss are combined into a multi-objective function. Pareto algorithm is developed by introducing the weighted-sum method with weight factors omega, so Pareto frontier solution composed of optimal solutions under different omega is obtained to reveal the trade-off relationship between single-objective functions. Material density controlled by the improved polynomial function is adopted as the design variable to control the catalyst distribution. Gradient information of the design variable is updated by using the adjoint-based sensitivity analysis method, and Helmholtz filter method is adopted to solve the problems of grayscale, unclear interface, and channel fracture. The results demonstrate that the GSF can reflect the scaling behavior of the topology optimization reactor. multi-objective functions under different omega are responded by changing the distribution of catalyst blocks and channel structure, which affects the diffusion and mixing of reactant. From the perspective of engineering application, this study provides a general and efficient new method for developing high-performance PBMRs.

关键词： Packed bed microfluidic reactor Topology optimization Microfluidic multi-objective optimization algorithm Reaction kinetic

来源：评论

学校读者我要写书评

暂无评论

Synergetic energy-conscious scheduling optimization of part feeding systems via a novel chaotic reference-guided policy

引用

ENGINEERING COMPUTATIONS 2022年第7期39卷 2655-2688页

作者： Zhou, Binghai Yi, Qi Li, Xiujuan Zhu, Yutong Tongji Univ Shanghai Peoples R China

Purpose This paper aims to investigate a multi-objective electric vehicle's (EV's) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism to coordinate multiple EVs is proposed to fulfill part feeding tasks. Design/methodology/approach A chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is constructed to deal with the problem. The proposed CRMSL benefits from the combination of reference vectors guided evolutionary algorithm (RVEA) and chaotic search. A novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy are then incorporated into the framework of the CRMSL to obtain satisfactory computational performance. Findings The involvement of the chaotic search and self-adaptive energy-efficient local search strategy contributes to obtaining a stronger global and local search capability. The computational results demonstrate that the CRMSL achieves better performance than the other two well-known benchmark algorithms in terms of four performance metrics, which is inspiring for future researches on energy-efficient co-scheduling topics in manufacturing industries. Originality/value This research fully considers the cooperation and coordination of handling devices to reduce energy consumption, and an improved multi-objective evolutionary algorithm is creatively applied to solve the proposed engineering problem.

关键词： Energy-efficiency Green manufacturing Synergetic scheduling multi-objective optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A novel single-DOF linkage-driven embracing gripper and its design method

引用

JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2025年第2期47卷 1-16页

作者： Zhang, Jingyao Yao, Jiantao Gao, Jianjian Yanshan Univ Sch Mech Engn Qinhuangdao 066004 Peoples R China Yanshan Univ Parallel Robot & Mechatron Syst Lab Hebei Prov Qinhuangdao 066004 Peoples R China

Single-degree-of-freedom (DOF) gripper is widely used in various robotic applications. As operational performance requirements and working environments evolve, single-DOF grippers are increasingly expected to position their drive motors closer to the robot base to reduce inertia, while simultaneously achieving a large gripping range and a compact folded size. This paper proposed a single-DOF linkage-driven gripper utilizing a linkage mechanism derived from the Sarrus mechanism as its driving input, which enhances its potential for application in certain parallel manipulators, thereby mitigating the impact of the gripper's drive motor inertia on the robot's response speed. Inspired by the grasping motion of the human hands, a multi-bar crank-slider finger structure was designed to achieve a large deployed size while maintaining a compact folded size. In order to imbue the designed manipulator with dexterity akin to that of a human hand, video footage was employed to capture the motion trajectories of human hands, which were then utilized as the anticipated motion trajectories for the manipulator's fingertips during the design phase. Furthermore, to achieve a compact form structure for the finger, a multi-objective optimization algorithm was employed to refine the finger structure of the manipulator. A prototype model was developed and subjected to gripping experiments, demonstrating its ability to perform effective gripping under the actuation of the linkages, while maintaining a compact folded size.

关键词： Embracing gripper Linkage structure multi-objective optimization algorithm Trajectory fitting Generalized parallel manipulators

来源：评论

学校读者我要写书评

暂无评论

BIM-based retrofit method (RBIM) for building envelope thermal performance optimization

引用

ENERGY AND BUILDINGS 2022年第0期256卷 111693-111693页

作者： Seghier, Taki Eddine Lim, Yaik-Wah Harun, Muhamad Farhin Ahmad, Mohd Hamdan Samah, Azurah A. Majid, Hairudin Abdul UCSI Univ Fac Engn Technol & Built Environm Sch Architecture & Built Environm Cheras Wilayah Perseku Malaysia Univ Teknol Malaysia UTM Fac Built Environm & Surveying Skudai Johor Malaysia UTM Inst Smart Infrastruct & Innovat Construct Isi Ctr Study Built Environm Malay World KALAM Skudai Johor Malaysia UTM Sch Comp Fac Engn Skudai Johor Malaysia

Retrofitting building envelope thermal performance is crucial to ensure adequate interior thermal com-fort of the building, minimize cooling load rate and, therefore, reduce overall building energy consump-tion. The retrofit process of an existing building envelope involves the design team selecting the best performing building materials and components based on various design variables and predefined design objectives. Worldwide, an overall thermal transfer value (OTTV) metric has been developed and used for assessing heat transfer through the building envelope. However, the application of OTTV implicates deal-ing with many design variables and much information, which makes the design decision-making process time-consuming and complicated. Furthermore, selecting the most appropriate design alternative in terms of OTTV performance while considering the most cost-effective design option could be a very chal-lenging task. Therefore, this research proposes a method for retrofitting building information modelling (RBIM) to achieve a tradeoff design set between two conflicting objectives, namely minimizing OTTV and minimizing the retrofit cost. The prototype system derived from this method integrates BIM authoring tools (e.g., Autodesk Revit (R)), visual scripting (e.g., Dynamo), and a non-dominated sorting genetic algo-rithm (NSGA-II) customized in MATLAB (R). The applicability of the proposed system is validated with a case study of an office building. The results show that the developed system provides a valuable design decision support system for retrofitting building envelope thermal performance while considering the retrofit cost. Additionally, the system shows an improved level of automation in terms of data manage-ment compared to the conventional methods. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Building information modelling multi-objective optimization algorithm Visual programming Overall thermal transfer value Green retrofitting Existing building

来源：评论

学校读者我要写书评

暂无评论

A newly combination model based on data denoising strategy and advanced optimization algorithm for short-term wind speed prediction

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第7期14卷 8271-8290页

作者： Lv, Mengzheng Wang, Jianzhou Niu, Xinsong Lu, Haiyan Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Univ Technol Fac Engn & Informat Technol Sch Software Sydney NSW Australia

Accurate implementation of short-term wind speed prediction can not only improve the efficiency of wind power generation, but also relieve the pressure on the power system and improve the stability of the grid. As is known to all, the existing wind speed prediction systems can improve the performance of the prediction in some sense, but at the same time they have some inherent shortcomings, just like forecasting accuracy is not high or indicators are difficult to obtain. In this paper, based on 10-min wind speed data from a wind farm, a new combination model is developed, which consists of three parts: data noise reduction techniques, five artificial single-model prediction algorithms, and multi-objective optimization algorithms. Through detailed and complete experiments and tests, the results demonstrate that the combination model has better performance than other models, solving the problem of instability of traditional forecasting models and filling the gap of low-prediction short-term wind speed forecasting.

关键词： Short-term wind speed forecasting Combination model Data denoising technique multi-objective optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

optimization of CMOS Voltage Reference with Blended Distribution Estimation Based on Parameter Evolutionary algorithms 66

Optimization of CMOS Voltage Reference with Blended Distribu...

引用

2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023

作者： Zeng, Yanhan Li, Chenglin Yu, Xiaofei Luo, Yanshen Zhao, Jiahui Lit, Jintao School of Electronics and Communication Engineering Guangzhou University Guangzhou China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen China

ISBN: (纸本)9798350302103

Manual optimization of parameters in analog integrated circuit (IC) design is a laborious and uncertain task, with no guarantee of achieving a good result. This paper presents an application of the blended distribution estimation evolutionary algorithm for parameter optimization in a dual-output voltage reference. To enhance the convergence speed and optimization effectiveness of automated IC design, such as voltage references, a blended distribution estimation evolutionary algorithm and an out-of-range (OOR) function based on performance ranking are proposed. The proposed methodology is demonstrated to significantly outperform the manual solution, with TC1, TC2, and power consumption improving by 34.97%, 17.08%, and 13.35%, respectively. Moreover, FoM1 and FoM2 are improved by 270.69% and 185.29%, respectively. © 2023 IEEE.

关键词： Circuit optimization Differential evolution Estimation of distribution multi-objective optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Application research of multi-objective Artificial Bee Colony optimization algorithm for parameters calibration of hydrological model

引用

NEURAL COMPUTING & APPLICATIONS 2019年第9期31卷 4715-4732页

作者： Huo, Jiuyuan Liu, Liqun Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China Gansu Agr Univ Coll Informat Sci & Technol Lanzhou 730070 Gansu Peoples R China

Parameter optimization methods for hydrological models have an important impact for the hydrological forecasting. To achieve the parameters' optimization and calibration for the distributed, conceptual watershed Xinanjiang model effectively and accurately, a multi-objective Artificial Bee Colony algorithm named RMOABC which adopts the mechanisms of regulation operator and Adaptive Grid is introduced in the paper. In the evolution of the algorithm, the regulation operator mechanism can balance the weights of local search and global search, and the Adaptive Grid mechanism is utilized to evaluate and maintain the Pareto solutions in the external archive. In the experiments, three commonly used multi-objective optimization algorithms, the NSGA-II, the epsilon-MOEA and the SMPSO, with the RMOABC algorithm were applied in Heihe River Basin, and the parameter optimization problem of Xinanjiang hydrological model was taken as the application case for long-term runoff prediction to validate and compare their performance. The experiments results showed the RMOABC algorithm can provide more comprehensive and reliable parameters sets for practical hydrological forecasting in the study area with lower execution time.

关键词： Hydrological model Parameter calibration multi-objective optimization algorithm Artificial Bee Colony algorithm

来源：评论

学校读者我要写书评

暂无评论

Strain Elastogram Improvement through Dynamic Programming Refinement using multi-objective optimization Technique 30

Strain Elastogram Improvement through Dynamic Programming Re...

引用

30th National and 8th International Iranian Conference on Biomedical Engineering (ICBME)

作者： Kalani, Mohammadhosein Fallah, Ali Rashidi, Saeid Dastjerdi, Maryam Mehdizadeh Amirkabir Univ Technol Fac Biomed Engn Tehran Iran Islamic Azad Univ Fac Med Sci & Technol Sci & Res Branch Tehran Iran

ISBN: (纸本)9798350359732;9798350359749

Elastograms suffer from noise and undesirable artifacts, making it necessary to enhance their signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for accurate detection of tissue deformations, determination of its displacement fields, and representation of tissue characteristics to medical professionals. This research aims to improve the quality of elastograms. Among various displacement estimation methods, the dynamic programming (DP) approach is chosen due to its superior speed, accuracy, and lower computational complexity and cost. The research objectives are defined as enhancing the SNR and CNR metrics and evaluating the resulting improvement in the elastogram quality. To achieve DP refinement, a perturbation is employed. The variables of the DP cost function are evaluated, and finally, the adjustment weight (w) is selected as a suitable criterion for optimization. After assessing different multi-objective optimization algorithms, the Electric Fish optimization algorithm with Non-dominated Sorting (NS-EFO) is chosen. In the designed optimization process, the DP cost function is considered as the objective function, SNR and CNR as objectives, and w as the design variable. To evaluate the improved performance of DP, two RF samples are used, recorded from a CIRS phantom and a Polyvinyl alcohol (PVA) single-layer mammary gland phantom. Following determining the optimal w, the SNR and CNR values are computed for both one-dimensional (1D) and two-dimensional (2D) models under different conditions. The results indicate that the SNR and CNR values for the CIRS phantom, on average, increased respectively by 556.74% and 853.72% in the 1D model and by 83.56% and 127.91% in the 2D model, compared to the primitive values. For the single-layer glandular phantom, the SNR and CNR values, on average, increased respectively by 66.98% and 33.03% in the 1D model and by 102.67% and 2178.99% in the 2D model, compared to the primary values.

关键词： Contrast to Noise Ratio Dynamic Programming Elastography multi-objective optimization algorithm PVA Phantom Signal to Noise Ratio Strain Elastogram

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：