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6th International Symposium on Computer Science and Intelligent Control (ISCSIC)

作者： Wei, Ping Lanzhou Resources & Environm Voc Tech Univ Coll Finance & Business Yellow River Basin Ecotope Integrat Ind & Educ Re 1173 Jiatan Lanzhou City 730000 Gansu Peoples R China

ISBN: (纸本)9781665454889

In order to better predict the education cost and improve the utilization rate of university funds, an education cost prediction model based on K-means clustering improved PSO-BP neural network was proposed. Firstly, aiming at the problem that BP neural network is prone to trap into local optimization and its convergence speed is slow, PSO was proposed to optimize the parameters of BP. Considering the limitations of PSO, the K-means clustering algorithm was added to cluster particle swarms, thereby enriching the diversity of the population. Finally, the improved particle swarm optimization algorithm was used for the education cost prediction of BP neural network. The results showed that the optimization performance of the improved particle swarm optimization algorithm is more stable and the convergence speed is faster. At the same time, in terms of prediction accuracy, the education cost prediction accuracy of the improved PSO-BP is higher.

关键词： BP neural network particle swarm optimization algorithm K-means clustering education cost prediction

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Prediction of the Water Cut with the Hybrid Optimized SVR 12th

Prediction of the Water Cut with the Hybrid Optimized SVR

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12th International Conference on Applications and Technologies in Information Security (ATIS)

作者： Pan, Shaowei Mou, Yuhui Zheng, Zechen Xian Shiyou Univ Xian 710065 Shaanxi Peoples R China

ISBN: (纸本)9789811911668;9789811911651

The moisture content identification of a water drive oilfield is related to how to formulate and adjust the development plan of the oil field. However, traditional prediction methods for the water cut usually have problems such as slow recognition speed and limited by the specific conditions of the oil well. Therefore, in order to avoid the influence of the above problems, a machine algorithm model of water cut based on hybrid optimization is proposed, which is based on the support vector regression (SVR) model. First, the data is constructed by time sliding window;secondly, on the basis of the fundamental SVR model, this paper combines the particle swarm optimization (PSO) and the Artificial Fish swarm algorithm (AFSA) to optimize the hyperparameters of the SVR prediction model to achieve better experimental results;finally, if the hybrid model proposed in this article has some good experimental results, then it can be applied to the actual water cut prediction of the oilfield. After comparing four different models, the prediction model based on the hybrid optimization algorithm proposed in this paper has some good experimental results. The prediction curve and the real curve have the same trend as a whole, and the subtle errors are also the smallest. Thus, it performs better than the SVR prediction model optimized by the differential evolution algorithm, the SVR prediction model optimized by the genetic algorithm, and the SVR prediction model optimized by the PSO.

关键词： Water cut identification particle swarm optimization algorithm Artificial Fish swarm algorithm SVR Prediction

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The State of Charge Estimation of Lithium-ion Batteries Using an Improved Extreme Learning Machine Approach 34

The State of Charge Estimation of Lithium-ion Batteries Usin...

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34th Chinese Control and Decision Conference (CCDC)

作者： He, Wei Ma, Hongyan Zhang, Yingda Wang, Shuai Dou, Jiaming Beijing Univ Civil Engn & Architecture Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China Natl Virtual Simulat Expt Ctr Smart City Educ Beijing 100044 Peoples R China

ISBN: (纸本)9781665478960

Accurate state of charge (SOC) estimation is of great significance for a lithium-ion battery to ensure its safe operation and prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner state of a battery cell, which cannot be directly measured. In order to improve the estimation accuracy of SOC, this paper develops a SOC estimation model for a lithium-ion battery using a particle swarm optimization-Extreme Learning Machine(PSO-ELM) algorithm. The PSO is applied to determine the optimal value of hidden layer neurons and the learning rate since these parameters are the most critical factors in constructing an optimal ELM model. The inputs to the PSO-ELM model are the battery voltage, current, and temperature, and the output is the actual SOC values. The performance of the proposed model is compared with BP neural network and ELM models and verified based on the mean square error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and SOC error. The results demonstrate that the PSO-ELM model offers higher accuracy and lower SOC error rate than ELM and BP neural network models.

关键词： Lithium-ion Battery State of Charge particle swarm optimization algorithm Extreme Learning Machine

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Short-term mathematical prediction model of air quality based on CEEMD-ELM-PSO

Short-term mathematical prediction model of air quality base...

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IEEE International Conference on Electrical Engineering, Big Data and algorithms (EEBDA)

作者： Qu, Hairong Zhang, Runnan Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Hohai Univ Coll Comp & Informat Nanjing Peoples R China

ISBN: (纸本)9781665416061

After entering the new era, people's living standard has been significantly improved, the concept of environmental protection has been deeply rooted. People pursue a greener and healthier lifestyle, and the concern for air quality has become more and more intense. People need to continuously analyze relevant mechanisms to help people predict air environment quality effectively and prevent relevant hazards in time. It is difficult to obtain a better prediction effect under different AQI fluctuation trends in the traditional single machine learning model for air quality prediction. In order to solve the problem, the prediction method is improved, and the ELM-PSO algorithm is used to predict the future AQI, which helps to analyze the future air change trends from a macro perspective. This bit combines Beijing's short-term air quality data and proposes an air quality short-term prediction model based on complementary ensemble empirical modal decomposition and an optimal limit learning machine. Firstly, the data are decomposed by CEEMD to reduce the non-smoothness of the data, thus reducing the impact of non-smoothness on the prediction accuracy;then ELM model prediction is performed for each decomposed series obtained;the output weights of the limit learning machine are used to PSO optimization search, and then construct the prediction model based on the limit learning machine;finally, all the prediction components are superimposed to obtain the final results. The results surface that the prediction model proposed in this paper has high accuracy in short-term air quality prediction.

关键词： Air quality prediction Extreme learning machine particle swarm optimization algorithm Complementary ensemble empirical modal decomposition Machine learning

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ANALYSIS AND RESEARCH ON OPTIMAL ECONOMIC OPERATION OF COGENERATION MICRO-GRID BASED ON HEAT PUMP CONTROL

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THERMAL SCIENCE 2021年第4期25卷 2871-2879页

作者： Xia, Wei Hua Henan Inst Econ & Trade Intelligent Finance Zhengzhou Henan Peoples R China Huazhong Univ Sci & Technol Hust Sch Econ Wuhan Hubei Peoples R China

When the micro-grid is connected to the grid, the influence of the load and the micropower supply's operating characteristics may cause the power exchange between the micro-grid and the large grid to cause unnecessary losses. The objective function of power supply satisfaction of large power grids. The article takes the cogeneration micro-grid as the research object. It proposes establishing an objective function including the satisfaction of micro-grid decision-makers, the satisfaction of social benefits, and the satisfaction of large-scale power supply using the analytic hierarchy process. After that, the article uses the particle swarm optimization algorithm combined with the typical dispatching strategy of the micro-grid to solve the aforementioned model with MATLAB as the primary tool, and obtains the 24 hour output optimization results of the micro-grid, and analyzes the results in detail. The calculation examples show that the model can achieve economic optimization based on satisfying the satisfaction of different groups, which verifies the scientificity and effectiveness of the proposed model.

关键词： heat pump control economic operation cogeneration type particle swarm optimization algorithm micro-grid

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Prediction of per capita water consumption for 31 regions in China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第23期28卷 29253-29264页

作者： Meng, Xiangmei Wu, Lifeng Hebei Univ Engn Coll Management Engn & Business Handan 056038 Peoples R China

Considering the shortage of per capita water resources in China, the paper established a fractional order accumulated grey prediction model (FGM(1,1)) to predict per capita water consumption of 31 regions (provinces, municipalities, and autonomous regions) in China from 2019 to 2024. The results show that per capita water consumption varies greatly across the different regions. Among them, per capita water consumption of nine regions (i.e., Beijing, Tianjin, Inner Mongolia, Jiangsu, Henan, Hubei, Guizhou, Yunnan, and Shaanxi) shows an increasing trend, whereas per capita water consumption in other 22 regions shows a downward trend. The predictive results can provide a basis for water resource management in China.

关键词： Per capita water consumption Water resources Fractional order accumulated gray prediction model particle swarm optimization algorithm

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Forecasting the renewable energy consumption of the European countries by an adjacent non-homogeneous grey model

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APPLIED MATHEMATICAL MODELLING 2021年 89卷 1932-1948页

作者： Liu, Lianyi Wu, Lifeng Hebei Univ Engn Coll Management Engn & Business Handan 056038 Peoples R China

In this paper, a new adjacent non-homogeneous grey model was proposed to predict renewable energy consumption in Europe. Based on the principle of adjacent accumulation, the proposed model emphasizes the weight relationship between the latest value and the historical data. Meanwhile, the heuristic algorithm is used to optimize the parameters of the proposed model. The model's performance was tested by the examples from four central European countries, including Austria, the Czech Republic, Hungary and Poland. In addition, the consumption of renewable energy in Europe and the world was predicted respectively, which showed that Europe's proportion of the world's renewable energy consumption will gradually decline. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Renewable energy consumption Adjacent accumulation Grey model European countries particle swarm optimization algorithm

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Design and optimization of power supply communication system for indoor wheeled inspection robots

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2025年

作者： Su, Renqi Shen, Hongming Huang, Yin Zhao, Shun Huaneng Zhejiang Energy Dev Co Ltd Clean Energy Branch Room 303Huaneng Bldg Hangzhou 310022 Peoples R China Huaneng Zhejiang Energy Dev Co Ltd Hangzhou Peoples R China

The existing power supply and communication system for indoor wheeled patrol robots relies on magnetic coupling mechanisms, but magnetic coupling mechanisms rarely meet the stability requirements of the system. In response to the challenge, a magnetic resonance-based wireless power and signal transmission system for robots is designed in this paper. It proposes transmitter coil switching control technology and receiver continuous energy harvesting technology, and establishes a communication model for one-to-many reception under different system states during motion. Based on this, a mathematical model for optimizing the structure of the magnetic coupling mechanism among transmitter and receiver coils was established. Optimal parameter values were determined using an enhanced particle swarm optimization algorithm (EAPSO). Experimental outcome implies that the transmission efficiency of the proposed method is 87.9%, representing an improvement of 4.8%-20.5%, and it can achieve efficient system stability control.

关键词： indoor wheeled patrol robot power supply and communication system magnetic coupling resonance particle swarm optimization algorithm transmitter and receiver coils

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ALLOCATION OF RES AND DSTATCOM FOR THE OPTIMAL DISTRIBUTION SYSTEM PLANNING 22

ALLOCATION OF RES AND DSTATCOM FOR THE OPTIMAL DISTRIBUTION ...

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22nd National Power Systems Conference (NPSC)

作者： Shukla, Madhu Jain, Prerna MNIT Jaipur Dept Elect Engn Jaipur India

ISBN: (纸本)9781665462020

The increasing demand of the load causes the voltage imbalance and increase in power loss. This is forcing the distribution companies to take the proper actions in the planning of distribution system. The allocation of Renewable Energy Source (RES) and Distribution Static Compensator (DSTATCOM) are considered in the distribution system planning to reduce the losses and maintain the voltage of the system within the required limit with the growing load demand. Due to the environmental concerns, the RES is becoming the must to consider as a part of the power system whereas the use of DSTATCOM is for the reactive power compensation to improve the power quality and the voltage profile. For the simultaneous allocation of RES and DSTATCOM in balanced Radial Distribution System (RDS), Loss Sensitivity Factor (LSF) used to get the candidate buses and particle swarm optimization (PSO) algorithm is used for optimal sizes. For the calculation of load flow of the system an efficient Backward-Forward Sweep Load Flow algorithm is used. The objective of the proposed methodology is to minimize the total cost which is the sum of total investment cost of RES and DSTATCOM, power purchase cost and pollution emission cost, keeping the voltage of each bus of the system within the limit. From the result obtained for the 38-bus balanced radial distribution system (RDS), it is clearly observed that the total active and reactive power losses are reduced and voltages of the system is improved with the optimal allocation of RES and DSTATCOM at minimum cost.

关键词： Renewable Energy Source Distribution Static Compensator Loss Sensitivity Factor particle swarm optimization algorithm Balanced RDS

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optimization of External Characteristic Parameters of Multiple Motors for Electric Vehicles Based on Optimal Torque Distribution Coefficient 25

Optimization of External Characteristic Parameters of Multip...

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25th International Conference on Electrical Machines and Systems (ICEMS)

作者： Su, Zhaorui Du, Jinhua Xi An Jiao Tong Univ State Key Lab Elect Insulat & Power Equipment Xian Peoples R China

ISBN: (纸本)9781665493024

In this paper, an optimization method based on optimal torque distribution coefficient is proposed for the optimization of external characteristic parameters of electric vehicles driven by multiple motors. Firstly, according to the performance requirements of a dual-motor driven electric vehicle, the original motor was matched. Then the law of motor efficiency Map changing with external parameters is described. An optimization algorithm is used to optimize the torque distribution coefficient of the electric vehicle in all working conditions. particle swarm optimization algorithm is used to optimize the external characteristic parameters of the two motors. Finally, a vehicle Simulink simulation platform is built to verify the correctness of the optimization results and the effectiveness of the optimization ideas.

关键词： motor efficiency Map optimal torque distribution coefficient particle swarm optimization algorithm Simulink

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