检索结果-内蒙古大学图书馆

2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Ponkumar, G. Jayaprakash, S. Hemkumar, P. School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India Panimalar Engineering College Chennai India

ISBN: (纸本)9798350325188

Accurate wind power assesses are fundamental to maximizing the use of wind power and defend a safe and reliable electricity grid. Gradient boosting algorithm with a limit learning machine optimized using particle swarm optimization (PSO-ELM), taking into account the inherent unpredictability and variability of wind power as well as the limitations of existing forecasting models. A Gradient Boost technique is then used to generate a large number of PSO-ELM weak predictors. Input weights and thresholds of each weak predictor, which each includes a unique hidden layer node. The results from each weak predictor are combined and weighed to get the final forecast result using a robust wind power forecast model. We use measured data from Turkish wind turbines to verify the efficacy of our suggested strategy. The findings demonstrate that the Gradient-PSO-ELM wind power prediction model has improved generalizability and accuracy than the Random forest. © 2023 IEEE.

关键词： and Wind power prediction Extreme Learning Machines Gradient Boost algorithm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

optimization Study of BP Neural Network Based on Genetic algorithm

Optimization Study of BP Neural Network Based on Genetic Alg...

引用

2023 IEEE International Conference on Electrical, Automation and Computer Engineering, ICEACE 2023

作者： Cai, Yongheng Zhou, Zhuye Li, Zehui School of Computing Zhuhai College of Science and Technology Zhuhai China School of Data Sciences Zhejiang University of Finance & Economics Hangzhou China School of Automation and Software Engineering Shanxi University Taiyuan China

ISBN: (纸本)9798350309614

In today's era of big data, neural networks play a pivotal role in fields such as image recognition and natural language processing. However, traditional Backpropagation (BP) neural networks often encounter challenges when dealing with complex problems, including slow convergence and getting stuck in local optima. To address these issues, this study employs a genetic algorithm as an optimization technique to optimize the weights and thresholds of BP neural networks. Training is conducted on a set of highly nonlinear mapping datasets to obtain the optimal weight and threshold configurations for the GA-BP model. Experimental results demonstrate that compared to traditional BP neural networks, the GA-BP model exhibits a 19.8% improvement in prediction accuracy, an approximately 9.20% increase in the coefficient of determination (R2), a reduction of approximately 62.50% in mean squared error (MSE), and a decrease of about 38.76% in root mean squared error (RMSE). Furthermore, the GA-BP model displays significantly accelerated convergence. The contribution of this research lies in proposing an optimization method that combines genetic algorithms with BP neural networks. This model not only enhances prediction accuracy compared to the standalone BP neural network but also significantly strengthens its generalization capabilities. As a result, it demonstrates substantial practical application potential across diverse datasets and application scenarios. © 2023 IEEE.

关键词： BP neural network genetic algorithm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Evaluation of an Electrical Equivalent Model Using Measurements and an optimization algorithm - Application to a Pyroelectric Sensor

引用

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2015年第7期24卷 1550097-1550097页

作者： Sifi, Nejmeddine Touayar, Oualid Univ Carthage Natl Inst Appl Sci & Technol Res Lab Mat Measurements & Applicat INSATDept Genie Phys & InstrumentatCtr Urbain N BP 676 Tunis 1080 Tunisia

A technique to estimate the parameters of an equivalent electrical model of a pyroelectric sensor prototype, based on measurements and an optimization algorithm, is presented. The dynamic behavior predicted by the equivalent electrical model is compared with measurements, and the effect of some geometrical mismatches is investigated as well. The optimization algorithm is a judicious combination of two search methods: random (a uniform statistical distribution) and deterministic (a conjugate gradient method). The reliability of the optimization algorithm to estimate the parameters values of the equivalent electrical model is also studied and discussed. The sensor equivalent electrical model can be used as a quick and intuitive analysis tool to allow the simulation of the device in a system-level design environment.

关键词： Electrical equivalent model parameters estimation optimization algorithm pyroelectric sensor measurement modeling

来源：评论

学校读者我要写书评

暂无评论

The optimization Clearing Model and algorithm Research of Electric Heat Storage Participates in Electricity Spot Market to Improve Renewable Energy Absorption Capacity 8

The Optimization Clearing Model and Algorithm Research of El...

引用

8th International Conference on Power and Renewable Energy, ICPRE 2023

作者： Liu, Gang Wang, Miao Zhao, Qingsong Wang, Zhonghui Zhang, Meishan Li, Dianyang State Grid LiaoNing Electric Power Supply Co. LTD Shenyang China State Grid LiaoNing Electric Power Supply Co. LTD Electric Power Research Insitute Shenyang China State Grid LiaoNing Electric Power Supply Co. LTD Economic Research Institute Shenyang China

ISBN: (纸本)9798350328813

With the construction of new power system and electricity market, the adjustable resources and market entities in the power system are constantly enriched, which puts forward higher requirements for system flexibility and market mechanism. Energy storage is an important means to improve the system regulation capacity, and low-cost, low-constraint electric heat storage technology is the best way to solve the problem of surplus renewable energy consumption and winter heating pollution. Therefore, this paper analyzes the operation characteristics of electric heat storage, proposes a method and mechanism to participate in the electricity spot market, and establishes an optimized clearing model including renewable energy consumption, electric heat storage load, cross-sectional constraint and node electricity price, on this basis, optimizes the operation mode of electric heat storage, improves the renewable energy absorption capacity, and reduces the overall operating cost and energy efficiency of the system. An example verifies the effectiveness of the proposed model and algorithm. © 2023 IEEE.

关键词： absorptive capacity clearing model electric heat storage electricity spot market optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Energy Management System for an Industrial Microgrid Using optimization algorithms-Based Reinforcement Learning Technique

引用

ENERGIES 2024年第16期17卷 3898页

作者： Upadhyay, Saugat Ahmed, Ibrahim Mihet-Popa, Lucian Ostfold Univ Coll Fac Informat Technol Engn & Econ Kobberslagerstredet 5 N-1671 Fredrikstad Norway

The climate crisis necessitates a global shift to achieve a secure, sustainable, and affordable energy system toward a green energy transition reaching climate neutrality by 2050. Because of this, renewable energy sources have come to the forefront, and the research interest in microgrids that rely on distributed generation and storage systems has exploded. Furthermore, many new markets for energy trading, ancillary services, and frequency reserve markets have provided attractive investment opportunities in exchange for balancing the supply and demand of electricity. Artificial intelligence can be utilized to locally optimize energy consumption, trade energy with the main grid, and participate in these markets. Reinforcement learning (RL) is one of the most promising approaches to achieve this goal because it enables an agent to learn optimal behavior in a microgrid by executing specific actions that maximize the long-term reward signal/function. The study focuses on testing two optimization algorithms: logic-based optimization and reinforcement learning. This paper builds on the existing research framework by combining PPO with machine learning-based load forecasting to produce an optimal solution for an industrial microgrid in Norway under different pricing schemes, including day-ahead pricing and peak pricing. It addresses the peak shaving and price arbitrage challenges by taking the historical data into the algorithm and making the decisions according to the energy consumption pattern, battery characteristics, PV production, and energy price. The RL-based approach is implemented in Python based on real data from the site and in combination with MATLAB-Simulink to validate its results. The application of the RL algorithm achieved an average monthly cost saving of 20% compared with logic-based optimization. These findings contribute to digitalization and decarbonization of energy technology, and support the fundamental goals and policies of the European Green Deal

关键词： EMS PPO BESS optimization algorithm peak shaving price arbitrage

来源：评论

学校读者我要写书评

暂无评论

Performance Analysis of an optimization Management algorithm on a Multi-generation Small Size Power Plant

引用

Energy Procedia 2016年 101卷 566-573页

作者： Piero Danti Lorenzo Pezzola Sandro Magnani Yanmar R&D Europe Firenze 50125 Italy

In the last years, the carbon footprint reduction has gained great relevance in the energy industry. Thus, it is necessary to choose approaches that weight the results not only evaluating economic benefits but also emphasizing the environmental impact. In order to measure this impact, the key parameter is the CO 2 emission in the atmosphere. The most powerful mean to satisfy this compromise between economic benefits and emission decrease is represented by the concept of Smart Grid. A Smart Grid implies a joint participation between information network and electric grid. In order to acquire the data from the electric grid, transmit them through the IT network, compute and translate them into commands to the plant devices, an ‘intelligent brain’ is necessary. In order to embed a small local network in the larger VPP a delocalized intelligent device is necessary, able to interface with the Smart Grid. An optimization algorithm performs this function of intelligent delocalized brain by setting different set-points for the energy devices on field. In this paper a purposefully developed optimization algorithm is described, with the aim of optimizing the operations of an existent trigeneration plant managing both RES and fossil energy sources. The plant analysed is a real plant located in central Italy, provided by several generators (PV, CHP, absorption chiller, electric chiller, gas boiler and a wind turbine). The results are yielded by a MATLAB/Simulink simulation tool, where all plant devices are characterized by datasheet information and on-field measurements. The benefits evaluation of the algorithm optimized management is obtained by embedding inside Simulink the optimization logic and executing it during the simulation runtime. The performance is compared with conventional thermal led management operations simulated in the same platform. The comparison is mainly based on economic costs but also considers CO 2 emissions and primary energy consumption. The analysis t

关键词： CHP optimization algorithm Genetic algorithm Thermal Led

来源：评论

学校读者我要写书评

暂无评论

Information Utilization Ratio in Heuristic optimization algorithms 13th

Information Utilization Ratio in Heuristic Optimization Algo...

引用

13th International Conference on Swarm Intelligence (ICSI)

作者： Li, Junzhi Tan, Ying Peking Univ Sch Artificial Intelligence Key Lab Machine Percept MOE Beijing 100871 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

Heuristic algorithms are able to optimize objective functions efficiently because they use intelligently the information about the objective functions. Thus, information utilization is critical to the performance of heuristics. However, the concept of information utilization has remained vague and abstract because there is no reliable metric to reflect the extent to which the information about the objective function is utilized by heuristic algorithms. In this paper, the metric of information utilization ratio (IUR) is defined, which is the ratio of the utilized information quantity over the acquired information quantity in the search process. The IUR proves to be well-defined. Several examples of typical heuristic algorithms are given to demonstrate the procedure of calculating the IUR. Empirical evidences on the correlation between the IUR and the performance of a heuristic are also provided. The IUR can be an index of how sophisticated an algorithm is designed and guide the invention of new heuristics and the improvement of existing ones.

关键词： optimization algorithm Information utilization Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

optimization of Intelligent Student Educational Smart Cloud Architecture based on Computer Topic Generation algorithm 5

Optimization of Intelligent Student Educational Smart Cloud ...

引用

5th International Conference on Inventive Computation Technologies (ICICT)

作者： Li, Cheng Nan Chong Vocat Coll Sci & Technol Nanchong 637200 Sichuang Peoples R China

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

optimization of the intelligent student educational intelligent cloud architecture based on computer topic generation algorithm is studied in the paper. There is no conflict between reverse proxy mode and packet filtering mode or ordinary proxy mode. These two modes can be used in firewall devices at the same time. The reverse proxy mode is used when the external network accesses the internal network. The forward proxy or packet filtering mode is used to reject other external access. Hence, for the designed model, this will be used as the cloud basis architecture. According to the regulatory cloud architecture design, its network can be divided into front-end service network and back-end resource high-speed synchronization network. This type of layered model is used to implement the intelligent student educational smart cloud architecture based on computer topic generation algorithm. Through the testing and implementation, the overall performance is presented.

关键词： optimization algorithm intelligent system cloud architecture computer topic generation smart education

来源：评论

学校读者我要写书评

暂无评论

Research on the optimization Method of Visual Sensor Calibration Combining Convex Lens Imaging with the Bionic algorithm of Wolf Pack Predation

引用

SENSORS 2024年第18期24卷 5926页

作者： Wu, Qingdong Miao, Jijun Liu, Zhaohui Chang, Jiaxiu Qingdao Univ Technol Sch Civil Engn Qingdao 266520 Peoples R China Shandong Luqiao Grp Co Ltd Jinan 250014 Peoples R China Shandong Univ Sci & Technol Sch Transportat Qingdao 266590 Peoples R China

To improve the accuracy of camera calibration, a novel optimization method is proposed in this paper, which combines convex lens imaging with the bionic algorithm of Wolf Pack Predation (CLI-WPP). During the optimization process, the internal parameters and radial distortion parameters of the camera are regarded as the search targets of the bionic algorithm of Wolf Pack Predation, and the reprojection error of the calibration results is used as the fitness evaluation criterion of the bionic algorithm of Wolf Pack Predation. The goal of optimizing camera calibration parameters is achieved by iteratively searching for a solution that minimizes the fitness value. To overcome the drawback that the bionic algorithm of Wolf Pack Predation is prone to fall into local optimal, a reverse learning strategy based on convex lens imaging is introduced to transform the current optimal individual and generate a series of new individuals with potential better solutions that are different from the original individual, helping the algorithm out of the local optimum dilemma. The comparative experimental results show that the average reprojection errors of the simulated annealing algorithm, Zhang's calibration method, the sparrow search algorithm, the particle swarm optimization algorithm, bionic algorithm of Wolf Pack Predation, and the algorithm proposed in this paper (CLI-WPP) are 0.42986500, 0.28847656, 0.23543161, 0.219342495, 0.10637477, and 0.06615037, respectively. The results indicate that calibration accuracy, stability, and robustness are significantly improved with the optimization method based on the CLI-WPP, in comparison to the existing commonly used optimization algorithms.

关键词： camera calibration optimization algorithm CLI-WPP reverse learning strategy reprojection error

来源：评论

学校读者我要写书评

暂无评论

Application of optimization algorithms to adaptive motion control for repetitive process

引用

ISA TRANSACTIONS 2021年 115卷 192-205页

作者： Szczepanski, Rafal Tarczewski, Tomasz Grzesiak, Lech M. Nicolaus Copernicus Univ Dept Automat & Measurement Syst Grudziadzka 5 PL-87100 Torun Poland Warsaw Univ Technol Inst Control & Ind Elect Koszykowa 75 PL-00662 Warsaw Poland

The application of optimization algorithms to adaptive motion control is proposed in this paper. In order to provide optimal system response, optimization algorithm is used as adjustment mechanism of controller coefficients. Most of optimization algorithms are not able to work in continuous optimization mode and with non-constant search space (i.e. dataset). For this reason, the introduction of a novel approach, Adaptive Procedure for optimization algorithms (APOA), that allows to apply most of optimization algorithms to adaptation process seems to be necessary. Such an algorithm is innovative, due to its universality in terms of applied optimization algorithm. Its application allows to obtain optimal motion control in modern electric drives. The proposed APOA is presented together with the novel desired-response adaptive system (DRAS) approach for repetitive processes. This solution provides unchanged system response regardless of plant parameters variation or external disturbances. The developed adaptive approach based on optimization algorithm is implemented in permanent magnet synchronous motor (PMSM) drive to adapt state feedback speed controller during moment of inertia variations. Since the proposed DRAS is model-free approach, the adaptation procedure is immune to issues related to plant parameters mismatch. The obtained results prove that proposed adaptive speed controller for PMSM assures desired system response regardless of the moment of inertia variation. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Adaptive controller optimization algorithm Repetitive process State feedback controller PMSM

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：