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AN EXTENDED CONFORMABLE FRACTIONAL ORDER GREY PREDICTION MODEL OF PER CAPITA ELECTRICITY CONSUMPTION FOR DAILY LIFE IN CHINA

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THERMAL SCIENCE 2025年第1期29卷 477-487页

作者： Jiang, Jianming Ban, Yandong Youjiang Med Univ Nationalities Sch Publ Hlth & Management Baise Peoples R China

Conformable fractional-order grey prediction models have attracted considerable attention due to their versatile modeling techniques. However, existing models often suffer from limitations in adaptability. To address this, this study proposes a new extended conformable fractional-order grey prediction model, namely the ECFGM(1,1) model. By integrating an adaptive weighting coefficient into the conformable fractional-order accumulation process, the model can effectively prioritize new information, thereby enhancing its rationality and adaptability. Moreover, the adjusted process can be tailored to either emphasize new information or adhere to traditional accumulation methods, which improves its adaptability. To verify the effectiveness of the ECFGM(1,1) model, ECFGM(1,1) is applied to two examples from the literature. The model evaluation results show that the ECFGM(1,1) model has higher fitting accuracy and predictive accuracy than the GM(1,1), CFGM(1,1), and NIPGM(1,1) models. Using the constructed ECFGM(1,1) for predictive analysis of the per capita electricity consumption for daily life in China, the results show that this model can capture the laws of its changes over time. Finally, per capita electricity consumption for daily life in China from 2022 to 2026 is predicted. The results show that by 2026, such consumption is estimated to reach 1165.35 KWh.

关键词： fractional-order grey prediction model new information priority particle swarm optimization algorithm per capita electricity consumption for daily life

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Cloud-based estimation of lithium-ion battery life for electric vehicles using equivalent circuit model and recurrent neural network

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JOURNAL OF ENERGY STORAGE 2025年 114卷

作者： Chen, Ziqing Chen, Jianguo Zhu, Zhicheng Chen, Jian Lv, Taolin Qiao, Dongdong Zheng, Yuejiu Univ Shanghai Sci & Technol Coll Mech Engn Shanghai 200093 Peoples R China Shanghai Inst Space Power Sources State Key Lab Space Power Sources Shanghai 200245 Peoples R China

With the increasing adoption of electric vehicles, the limitations of BMS in terms of storage capacity and computational power lead to a gradual accumulation of errors in battery capacity estimation over time. This growing inaccuracy significantly compromises the effective management of on-board power battery states. The challenge of battery capacity estimation based on extensive cloud-stored data has become a key focus in current research. In this paper, we propose an enhanced method that combines the ECM with a RNN to address this issue. By incorporating the relationship between OCV and SOC into the ECM, and employing PSO for direct capacity identification, we achieve accurate estimation. Furthermore, the use of RNN dynamically adjusts the observation noise in Kalman filtering, significantly improving the precision of the estimation. Experimental results demonstrate that the proposed `method yields a RMSE of <3 % and an average relative error below 2 %, compared to traditional approaches. This study presents a high-precision, efficient solution for estimating battery capacity using cloud-based data from electric vehicles, offering substantial application value.

关键词： Electric vehicle cloud data Equivalent circuit model particle swarm optimization algorithm Recurrent neural network

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Development and simulation of two novel indoor odor source localization methods using a modified shark smell optimization algorithm

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MEASUREMENT 2025年 240卷

作者： Lin, Qin Wu, Sihuan Wu, Sifan Wang, Hui Zhang, Jinxiu Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen Campus Shenzhen 518107 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai 519085 Peoples R China

Aiming to solve the problem of odor source localization (OSL) in the presence of interference sources, this paper presents two methods based on swarm intelligence algorithms. We initially introduced the shark smell optimization (SSO) algorithm and modified it for OSL tasks. Subsequently, mechanisms for collective information sharing and preventing falling into local minima were incorporated, leading to the development of the Improved Shark Smell optimization (I-SSO) algorithm. We tested both algorithms in a computational fluid dynamics (CFD) simulated environment with a single interference source and compared them to the particle swarm optimization (PSO) algorithm and whale optimization algorithm (WOA). In scenarios with one and two interference sources. The results showed that the I-SSO algorithm outperformed the other three algorithms in both environment settings, demonstrating a higher success rate and superior search distance efficiency.

关键词： Odor source localization particle swarm optimization algorithm Shark smell optimization algorithm Whale optimization algorithm swarm intelligence

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A simplified swarm optimization-based restricted Boltzmann machine algorithm for a time series clustering

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APPLIED SOFT COMPUTING 2025年 173卷

作者： Zulvia, Ferani E. Kuo, R. J. Inst Teknol Bandung Dept Ind Engn Jalan Ganesha 10 Bandung 40132 Indonesia Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei 106 Taiwan

Time series clustering is an important area of research, driven by the prevalence of time series data in domains such as power and maintenance data. However, most existing clustering algorithms are not specialized for time series data. Therefore, this study introduces a metaheuristics-based approach, utilizing a restricted Boltzmann machine (RBM) for feature extraction in time series clustering. The RBM, a neural network algorithm with two layers and undirected weights, is kept simple to avoid complex parameter settings. The study employs the RBM as an encoder, coupled with a K-step contrastive divergence and an improved, simplified swarm optimization (iSSO) algorithm for training. The proposed hybrid of iSSO-based RBM and K-means algorithm is compared with RBM and particle swarm optimization-based RBM across the tested time series datasets. Results indicate that the proposed algorithm yields superior performance, reconstructing time series data with minimal error and achieving the highest clustering accuracy compared to other baseline algorithms.

关键词： Feature-based time series clustering Restricted Boltzmann machine Simplified swarm optimization algorithm particle swarm optimization algorithm

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Thermodynamic performance prediction and optimization of a 1 kW ocean thermal energy cogeneration system based on artificial neural network

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ENERGY 2025年 314卷

作者： Zhang, Yuan Li, Yifan Tian, Zhen Yang, Chao Peng, Hao Kan, Ankang Gao, Wenzhong Shanghai Maritime Univ Coll Merchant Marine Shanghai 201306 Peoples R China Hainan Univ State Key Lab Marine Resource Utilizat South China Haikou 570228 Peoples R China

In this paper, a prediction model based on the artificial neural network (ANN) method was constructed, and the thermodynamic performance prediction and optimization were conducted for a system for ocean thermal energy conversion that integrates an air conditioning cycle (OTEC-AC). By employing the variable importance measure (VIM) method, ten key parameters were selected as input parameters, and three output parameters, including system power generation (Wele), cold energy efficiency (eta co), and energy efficiency (eta en) were defined. The accuracies of prediction models constructed by the back propagation neural network (BPNN) and BPNN optimized by the particle swarm optimization algorithm (BPNN-PSO) were compared. Analysis of the thermodynamic characteristics and multi-objective optimization of the system were performed, and performance differences between the experimental data-based and the BPNN-PSO model-based system were compared. Results showed that optimizing the initial weights and thresholds of the model using the PSO algorithm could enhance the model's generalization capability. Multi-objective optimal results based on prediction data were very close to those based on experimental data, with a maximum relative error of 4.4498 %. This study validates the feasibility of using the ANN model for data prediction in OTEC cogeneration systems, which could provide guidelines for the optimal design of related systems.

关键词： OTEC Air conditioning Artificial neural network particle swarm optimization algorithm Multi-objective optimization

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Soil moisture retrieval using GNSS signal-to-noise ratio data based on an improved optimal arc selection method

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GPS SOLUTIONS 2025年第1期29卷 1-14页

作者： He, Jiaxing Zheng, Nanshan Ding, Rui Liu, Xuexi China Univ Min & Technol Sch Environm & Spatial Informat Xuzhou 221116 Peoples R China China Univ Min & Technol Key Lab Land Environm & Disaster Monitoring Minist Nat Resources Xuzhou 221116 Peoples R China

Global Navigation Satellite System-interferometric reflectometry (GNSS-IR) can be used to monitor soil moisture by establishing a relationship between phase and soil moisture. Therefore, the accuracy of the phase value is very important. However, topography and vegetation can introduce errors in the phase values when processing the raw signal-to-noise ratio reflection component (SRC). This study proposes an optimal arc selection (OAS) method to overcome this limitation. The novelty of this method is the use of entropy to evaluate the accuracy of curve fitting and the use of a particle swarm optimization algorithm to search for the optimal elevation range of SRC. We processed SNR data from 3 GNSS stations and provided the verification results through in-situ soil moisture measurements. The results showed that the phase values calculated using the OAS method were more accurate than those calculated using the conventional method. The new method improved the agreement between GNSS-derived soil moisture and in-situ measurements, with a reduction of 29% in root mean square error and 31% in mean absolute error. This suggests that the OAS method can improve the capacity of soil moisture retrieval in undulating terrain areas and promote the development of GNSS-IR.

关键词： GNSS-IR Soil moisture particle swarm optimization algorithm Entropy method

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Design of backstepping control for nonlinear systems based on command filter and uncertainty estimation

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ASIAN JOURNAL OF CONTROL 2025年第0期

作者： Sun, Yongbo Zhou, Lan Jiang, Fuxi Li, Meiliu Xiao, Wenbin Hunan Univ Sci & Technol Sch Mech Engn Xiangtan Peoples R China Hunan Univ Sci & Technol Sch Informat & Elect Engn Xiangtan 411201 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan Peoples R China

This paper presents an improved command-filtered backstepping control strategy for a class of multisource-disturbed nonlinear systems based on an active-uncertainty-estimation-and-compensation approach. Exploiting the available model information of a plant, a reduced-order extended-state observer is designed to estimate a matched total uncertainty. Command filters are constructed to estimate virtual control inputs. Both the estimated states and estimation errors are used for the construction of the backstepping control law, which guarantees the stability of the system and compensates for uncertainties. Moreover, a particle swarm optimization algorithm is adopted to simultaneously optimize the state observer gain and backstepping control gains. Finally, a case study on a rotational system shows that the presented method achieves better uncertainty suppression and tracking performance in both transient and steady state than other related methods.

关键词： backstepping control extended state observer particle swarm optimization algorithm tracking control uncertainty compensation

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Prediction of rotary seal leakage rate under the influence of stress relaxation based on G A-PSO-BP hybrid algorithm

ENGINEERING RESEARCH EXPRESS

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ENGINEERING RESEARCH EXPRESS 2025年第1期7卷

作者： Zhang, Yi He, Wei Hu, Ling Lou, Zicheng Zhang, Daqin Wang, Zheng Southwest Petr Univ Sch Mechatron Engn Xindu Ave 8 Chengdu 610500 Sichuan Peoples R China

Rotary combination seals of composite impactors are susceptible to failure and relaxation in high-pressure, high-temperature deep-well environments. The aim of this study is to analyze the effect of stress relaxation on the performance of the seals and to assess their performance under different operating conditions. The study adopts GA-PSO-BP algorithm to predict the leakage, simulates the contact pressure change of the sealing surface before and after stress relaxation by Abqus software, and calculates the leakage rate by using MATLAB software in combination with the thermoelastic flow lubrication model and the finite element analysis data, to establish the prediction model dataset. The study also optimised the neural network prediction model and verified the accuracy of the model experimentally. The results show that increasing pressure leads to an increase in contact pressure, but relaxation increases contact length and decreases maximum pressure, and increasing compression increases and then decreases contact pressure and increases the peak value, but relaxation decreases the peak value and increases the risk of leakage. The increase in speed and temperature both lead to an increase in leakage rate, and the GA-PSO-BP model predicts more accurately the rotary combination seals with a higher leakage rate after considering the stress relaxation.

关键词： rotary combination seal stress relaxation BP neural network genetic algorithm particle swarm optimization algorithm leakage

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particle swarm and bat-inspired optimization algorithm-based parameter identification of Krasnosel’skii-Pokrovskii model for piezoelectric hysteresis

Particle swarm and bat-inspired optimization algorithm-based...

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第五届国际仿生工程学术大会(ICBE2016)

作者： Rui Xu Miaolei Zhou Yantao Tian Department of control science and engineering Jilin University

The piezoelectric ceramics is a kind of novel intelligent materials and possesses advantages of ultrafine resolution, high stiffness and fast frequency response. It is an excellent choice as an actuator to be applied widely to micro-nano positioning system. However, one of the drawbacks of the piezoelectric actuator is the obvious hysteresis nonlinearity to limit the accuracy of positioning. The present study focuses on describing the hysteresis nonlinearity between input voltage and output displacement of piezoelectric ceramics. In order to investigate the hysteresis nonlinearity of piezoelectric ceramics, a series of models are presented, such as BoucWen model, Preisach model, Krasnosel’skii-Pokrovskii model and so on. However, the main difficulty is how to select an effective method to identify the unknown parameters of these hysteresis nonlinearity models. In this paper, we propose a novel hybrid optimization algorithm of particle swarm and bat-inspired to identify the density function of Krasnosel’skii-Pokrovskii model. The proposed hybrid optimization algorithm has the fast convergence of bat-inspired and the global search ability of particle swarm. During operation of the optimization algorithm, we divided the swarm into two different parts randomly as the initial value. One set of data is optimized by particle swarm algorithm and find the optimal value i.e. best1, the other set of data is optimized by bat-inspired algorithm and the optimal value is best2. Then we select the bigger one between best1 and best2 i.e. Gbest as the global optimal value of the two algorithms for the next iteration *** taking advantage of the optimization algorithm, an objective function is defined firstly. Then the density function of Krasnosel’skii-Pokrovskii model is identified by using bat-inspired algorithm and hybrid optimization algorithm of particle swarm and bat-inspired respectively. The simulations show that the displacement error of identification of Kras

关键词： particle swarm optimization algorithm bat-inspired algorithm Krasnosel’skii-Pokrovskii model

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A Wind Speed Prediction Method Based on Signal Decomposition Technology Deep Learning Model

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ENERGIES 2025年第5期18卷 1136-1136页

作者： Du, Jie Chen, Shuaizhi Pan, Linlin Liu, Yubao Nanjing Univ Informat Sci & Technol Sch Software Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Precis Reg Earth Modeling & Informat Ctr Nanjing 210044 Peoples R China China Elect Power Res Inst Beijing 100192 Peoples R China Nanjing Univ Informat Sci & Technol Sch Atmospher Phys Nanjing 210044 Peoples R China

Accurate and reliable wind speed prediction plays a significant role in ensuring the reasonable scheduling of wind power resources. However, wind speed sequences often exhibit complex characteristics such as instability and volatility, which create substantial challenges for prediction. In order to cope with these challenges, a multi-step wind speed prediction method based on secondary decomposition (SD) techniques and deep learning prediction models is proposed in this paper. First, the original signal was decomposed into multiple sequences by using two signal decomposition techniques, multi-scale wavelet power spectrum analysis (MWPSA) and variational mode decomposition (VMD). Second, a model was constructed by combining convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, and attention mechanism to perform multi-step wind speed predicting for each sequence, and the model parameters were optimized by the particle swarm optimization (PSO) algorithm. Ultimately, the results from all sequences were combined to generate the final wind speed prediction. The predictive performance of the proposed method was evaluated using real wind speed data collected from a wind farm in China. Experimental results show that the proposed method significantly outperforms other comparison models in multi-step wind speed prediction, which highlights its accuracy and reliability.

关键词： wind speed predicting signal decomposition hybrid deep learning model particle swarm optimization algorithm

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