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Prediction of electricity consumption based on GM(1,Nr) model in Jiangsu province, China

ENERGY 2023年第PartA期262卷

作者： Du, Xiaoyi Wu, Dongdong Yan, Yabo Jiangnan Univ Sch Business Wuxi 214122 Jiangsu Peoples R China Nankai Univ Coll Tourism & Serv Management Tianjin 300350 Peoples R China

In this paper, GM(1,N-r) model is established to improve the traditional GM(1,N) model from three aspects: (1) transforming the original sequence to satisfy the modeling conditions with particle swarm optimization algorithm;(2) introducing grey incidence analysis to obtain the grey incidence ranking and carrying out stepwise test for significant variables to determine the number of variables;and (3) predicting the related factor sequence through the improved GM(1,1) model. Empirical analysis shows that the proposed GM(1,N-r) model has remarkable good prediction performance compared with the traditional grey forecasting model. It is also demonstrated that the extraction of influencing factors can significantly improve the prediction effectiveness, especially when pursuing the best fitting effect on small sample data. The findings indicate that the electricity consumptions of Jiangsu Province in the next several years will be at a high level and keep rising, with a predicted value of 9712.48 billion kilowatt-hours in 2030. The findings can help the government and energy related institutions to develop management policies on energy demand, and the proposed model can also be extended for the application in other regions.

关键词： Electricity consumptions forecasting Grey forecasting model GM(1,N-r) model Grey incidence analysis particle swarm optimization algorithm

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A novel integrated hydrogen and natural gas liquefaction process utilizing a modified double mixed refrigerant process pre-cooling system

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APPLIED THERMAL ENGINEERING 2023年 224卷

作者： Geng, Jinliang Sun, Heng China Univ Petr Natl Engn Res Ctr Oil & Gas Pipeline Transportat S MOE Key Lab Petr Engn Beijing Key Lab Urban Oil & Gas Distribut Technol Beijing 102249 Peoples R China

The hydrogen liquefaction process is energy-intensive due to the lower liquefaction temperature. To improve the energy efficiency of the hydrogen liquefaction process, a novel integrated liquefaction process employing a modified double mixed refrigerant process pre-cooling is proposed and analyzed. The three-stage adiabatic ortho-para hydrogen conversion makes the process compact. Furthermore, the particle swarm optimization algorithm is utilized to find the optimal decision variables. The result reveals that the total specific energy consumption (SEC) of the process is 6.5921 kWh/kgLH2-LNG, which is 15.83% lower than the base case. The SECs for the production of LNG and LH2 are 0.2445 kWh/kgLNG and 6.3476 kWh/kgLH2. Moreover, the exergy analysis and thermodynamic evaluation reveal that the exergy efficiency, coefficient of performance, and figure of merit of the process are 49.26%, 0.2306, and 0.49, exhibiting high thermodynamic performance. Finally, the sensi-tivity analysis indicates that the evaporation pressure of the sub-cooling refrigerant has the greatest impact on the performance of the process. The proposed process has an important reference value in the design of the combined production of LH2 and LNG from industrial by-products and the H2 liquefaction process.

关键词： Integrated liquefaction process Mixed refrigerant Liquefied hydrogen Liquefied natural gas particle swarm optimization algorithm

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Quantitative determination of TVB-N content for different types of refrigerated grass carp fillets using near-infrared spectroscopy combined with machine learning

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JOURNAL OF FOOD COMPOSITION AND ANALYSIS 2024年 126卷

作者： Zhang, Qinyu Hu, Zhigang Xu, Zijian Zhang, Peilin Jiang, Yajun Fu, Dandan Chen, Yan Wuhan Polytech Univ Sch Mech Engn Wuhan 430023 Hubei Peoples R China Hubei Prov Cereal & Oil Machinery Engn Technol Res Wuhan 430023 Hubei Peoples R China Hubei Prov Aquat Prod Proc Equipment Engn Technol Wuhan 430023 Hubei Peoples R China

This research aims to accurately forecast the freshness indicators (TVB-N) of skinned and skinless grass carp fillets by integrating near-infrared spectroscopy (NIR) with machine learning algorithms. By comparing the predictive accuracy of machine learning models for the two types of grass carp fillets, the most effective modeling method is identified. Methodologically, the study first applies orthogonal signal correction (OSC) and the first derivative among other algorithms for spectral pre-processing. Subsequently, competitive adaptive reweighted sampling (CARS), moving window partial least squares (MWPLS), and random frog (RF) are used for the selection of variables. Lastly, partial least squares regression (PLSR), support vector regression (SVR), backpropagation neural networks (BPNN), and particle swarm optimization-enhanced BP neural networks (PSO-BP) are employed to quantitatively analyze the NIR data. The most relevant results reveal that the (OSC+D1)-CARSPSO-BP model exhibits superior predictive capabilities. Especially when applied to skin-on fish fillets (R2P =0.988, RMSEP=0.092), this model surpasses that for skinless fish fillet data (R2P =0.987, RMSEP=0.096). Therefore, combining near-infrared with machine learning to predict the freshness (TVB-N) of grass carp fillets based on skin-on samples is a more effective non-destructive testing method.

关键词： Near -Infrared spectroscopy Rapid non-destructive detection Grass carp fillets Total volatile basic nitrogen Machine learning Orthogonal signal correction (OSC) particle swarm optimization algorithm optimized BP neural network (PSO-BP)

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Hybrid Structured Artificial Network For Compressive Strength Prediction Of HPC Concrete

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淡江理工学刊 2023年第7期26卷 989-999页

作者： Liang Chen

This study aims to develop a surrogate artificial neural network-based technique for predicting the compressive strength of concrete, which is the most significant factor in the life service of concrete and its durability in civil construction projects of civil engineering. For this goal, a data set for high-performance concrete is gathered from the literature repository, which includes a different percentage of fly ash, silica fume, and superplasticizer in the mix designs. The data set is applied to train and validate the optimal structured artificial neural network optimized by innovative equilibrium optimization and particle swarm optimization algorithms. The results showed that the EOANN-I and PANN-I model with the R2 and RMSE values of 0.9889, 1.771, 0.9881, and 1.8813, stood at the first and second stage of the most capable models in the prediction of HPC compressive strength. Although the PANN-I model's complexity is lower than the EOANN-I model, the rate of its prediction accuracy covers its lack of complexity.

关键词： HPC concrete compressive strength artificial neural network equilibrium optimization algorithm particle swarm optimization algorithm

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An Improved particle swarm optimization algorithm

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International Conference on Mechanical Engineering and Intelligent Systems (ICMEIS 2012)

作者： Jiang, Changyuan Zhao, Shuguang Guo, Lizheng Ji, Chuan Donghua Univ Coll Informat Sci & Technol Shanghai 201600 Peoples R China

ISBN: (纸本)9783037854693

Based on the analyzing inertia weight of the standard particle swarm optimization (PSO) algorithm, an improved PSO algorithm is presented. Convergence condition of PSO is obtained through solving and analyzing the differential equation. By the experiments of four Benchmark function, the results show the performance of S-PSO improved more clearly than the standard PSO and random inertia weight PSO. Theoretical analysis and simulation experiments show that the S-PSO is efficient and feasible.

关键词： particle swarm optimization algorithm inertia weight sinusoidal changing differential equation

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Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction

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SCIENTIFIC REPORTS 2024年第1期14卷 1-20页

作者： Li, Chengjie Wang, Yanglin Meng, Linghui Zhong, Wen Zhang, Chengfang Liu, Tao Southwest Minzu Univ Sch Comp & Artificial Intelligence Key Lab Comp Syst State Ethn Affairs Commiss Chengdu 610041 Peoples R China Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Chengdu Third Peoples Hosp Dept Gen Med Chengdu 610014 Peoples R China Sichuan Police Coll Intelligent Policing & Natl Secur Risk Management Intelligent Policing Key Lab Sichuan Prov Luzhou Peoples R China

Coronary artery disease represents a formidable health threat to middle-aged and elderly populations worldwide. This research introduces an advanced BP neural network algorithm, EPSOSA-BP, which integrates particle swarm optimization, simulated annealing, and a particle elimination mechanism to elevate the precision of heart disease prediction models. To address prior limitations in feature selection, the study employs single-hot encoding and Principal Component Analysis, thereby enhancing the model's feature learning capability. The proposed method achieved remarkable accuracy rates of 93.22% and 95.20% on the UCI and Kaggle datasets, respectively, underscoring its exceptional performance even with small sample sizes. Ablation experiments further validated the efficacy of the data preprocessing and feature selection techniques employed. Notably, the EPSOSA algorithm surpassed classical optimization algorithms in terms of convergence speed, while also demonstrating improved sensitivity and specificity. This model holds significant potential for facilitating early identification of high-risk patients, which could ultimately save lives and optimize the utilization of medical resources. Despite implementation challenges, including technical integration and data standardization, the algorithm shows promise for use in emergency settings and community health services for regular cardiac risk monitoring.

关键词： particle swarm optimization algorithm Simulated annealing algorithm Principal component analysis Elimination strategy Feature engineering Cardiovascular disease (CVD)

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Metaheuristic-based possibilistic fuzzy k-modes algorithms for categorical data clustering

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INFORMATION SCIENCES 2021年 557卷 1-15页

作者： Kuo, R. J. Zheng, Y. R. Thi Phuong Quyen Nguyen Natl Taiwan Univ Sci & Technol Dept Ind Management 43Sect 4Kee Lung Rd Taipei 106 Taiwan Univ Sci & Technol Univ Danang Fac Project Management 54 Nguyen Luong Bang Danang Vietnam Texas Instrument Taiwan Ltd New Taipei Taiwan

Smart devices and technology applications are used in many fields. Much information is now recorded and collected rapidly so data analysis, especially clustering analysis, is vital to the process of analyzing and obtaining valuable information from datasets. However, data has different types of attributes: numerical, categorical, and mixed attributes. Some datasets also contain noise and outliers. An appropriate clustering is necessary to exploit the data structure. This study proposes a clustering algorithm that is called a possibilistic fuzzy k-modes (PFKM) algorithm. This combines the concept of possibility with the fuzzy k-modes (FKM) algorithm to address the effect of outliers and to improve the clustering results for categorical data. This study also implements three metaheuristics to increase clustering performance: a genetic algorithm (GA), a particle swarm optimization (PSO) and the sine-cosine algorithm (SCA). Three clustering algorithms are proposed: the GAPFKM, PSO-PFKM, and SCA-PFKM algorithms. The performance of the algorithms is compared with that for the classical FKM algorithm using two indices: the sum-of-squared error (SSE) and the accuracy. The experimental results show that the PSO-PFKM and SCA-PFKM algorithms perform better for most datasets. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Clustering analysis Categorical data Metaheuristic Genetic algorithm particle swarm optimization algorithm Sine cosine algorithm Fuzzy clustering Fuzzy k-modes algorithm

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A Prediction Model of Coal Seam Roof Water Abundance Based on PSO-GA-BP Neural Network

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WATER 2023年第23期15卷 4117-4117页

作者： Dai, Xue Li, Xiaoqin Zhang, Yuguang Li, Wenping Meng, Xiangsheng Li, Liangning Han, Yanbo China Univ Min & Technol Sch Resources & Geosci Xuzhou 221116 Peoples R China State Investment Hami Energy Dev Co Ltd Hami 839000 Peoples R China

With the gradual increase of coal production capacity, the issue of water hazards in coal seam roofs is increasing in prominence. Accurate and effective prediction of the water content of the roof aquifer, based on limited hydrogeological data, is critical to the identification of the central area of prevention and control of coal seam roof water damage and the reduction of the incidence of such accidents in coal mines. In this paper, we establish a prediction model for the water abundance of the roof slab aquifer, using a PSO-GA-BP neural network. Our model is based on five key factors: aquifer thickness, permeability coefficient, core recovery, number of sandstone and mudstone interbedded layers, and fold fluctuation. The model integrates the genetic algorithm (GA) into the particle swarm optimization (PSO) algorithm, with the particle swarm optimization algorithm serving as the primary approach. It utilizes adaptive inertia weight and quadratic optimization of the weights and thresholds of the backpropagation neural network to minimize the output error threshold for the purpose of minimizing output errors. The prediction model is applied to hydrogeology and coal mine production for the first time. The model is trained using 100 data samples collected by the Surfer 13 software. These samples help to accurately predict the unit inflow of water. The model is then compared with traditional forecasting methods such as FAHP, BP, and GA-BP neural network models to determine its efficiency. The study found that the PSO-GA-BP neural network model accurately predicts aquifer water abundance with higher precision. The root mean square error (RMSE) of the test set is determined to be 8.7 x 10-4, and the fitting result is measured at 0.9999, indicating minimal error with actual values of the sample. According to the prediction results of the test set, the water abundance capacity of the No. 7 coal mine in Hami Danan Lake is divided, and it is found that the overall difference

关键词： water abundance particle swarm optimization algorithm genetic algorithm BP neural network FAHP

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Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network

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SENSORS 2023年第3期23卷 1097-1097页

作者： Fang, Jun Deng, Zhiyang Tu, Jun Song, Xiaochun Hubei Univ Technol Sch Mech Engn Wuhan 430068 Peoples R China Hubei Key Lab Modern Mfg Qual Engn Wuhan 430068 Peoples R China

To address the problem of the quantitative identification of glass panel surface defects, a new method combining the chaotic simulated annealing particle swarm algorithm (CSAPSO) and the BP neural network is proposed for the quantitative evaluation of microwave detection signals of glass panel defects. First, the parameters of the particle swarm optimization (PSO) algorithm are dynamically assigned using chaos theory to improve the global search capability of the PSO. Then, the CSAPSO-BP neural network model is constructed, and the return loss and phase of the microwave detection echo signal of glass panel defects are extracted as the input feature quantity of the network, from which the intrinsic connection between input and output is found through network training and testing to achieve the prediction of the depth and width of glass panel surface defects. The results show that the CSAPSO-BP network model can more accurately characterize the defect geometry of glass panels than the PSO-BP network model.

关键词： glass panel chaos simulated annealing algorithm particle swarm optimization algorithm microwave detection quantitative identification

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Based on the Improved PSO-TPA-LSTM Model Chaotic Time Series Prediction

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ATMOSPHERE 2023年第11期14卷 1696-1696页

作者： Cai, Zijian Feng, Guolin Wang, Qiguang Yangzhou Univ Coll Phys Sci & Technol Yangzhou 225002 Peoples R China China Meteorol Adm Natl Climate Ctr Lab Climate Studies Beijing 100081 Peoples R China China Meteorol Adm China Meteorol Adm Training Ctr Beijing 100081 Peoples R China

In order to enhance the prediction accuracy and computational efficiency of chaotic sequence data, issues such as gradient explosion and the long computation time of traditional methods need to be addressed. In this paper, an improved particle swarm optimization (PSO) algorithm and Long Short-Term Memory (LSTM) neural network are proposed for chaotic prediction. The temporal pattern attention mechanism (TPA) is introduced to extract the weights and key information of each input feature, ensuring the temporal nature of chaotic historical data. Additionally, the PSO algorithm is employed to optimize the hyperparameters (learning rate, number of iterations) of the LSTM network, resulting in an optimal model for chaotic data prediction. Finally, the validation is conducted using chaotic data generated from three different initial values of the Lorenz system. The root mean square error (RMSE) is reduced by 0.421, the mean absolute error (MAE) is reduced by 0.354, and the coefficient of determination (R2) is improved by 0.4. The proposed network demonstrates good adaptability to complex chaotic data, surpassing the accuracy of the LSTM and PSO-LSTM models, thereby achieving higher prediction accuracy.

关键词： chaotic sequence particle swarm optimization algorithm time-mode attention mechanism long short-term memory Lorenz system

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