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18th International Conference on Intelligent Computing (ICIC)

作者： Zhang, Xing Zhang, Wensong Sinounited Investment Grp Corp Ltd Beijing 102611 Peoples R China Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China

ISBN: (纸本)9783031138324;9783031138317

Carbon trading market control and reduce greenhouse gas emissions by market mechanism, which is an important policy tool to achieve carbon peak and carbon neutrality. Carbon price is a core element of carbon market, its accurate prediction is of great significance for carbon market risk management. A novel carbon price prediction model is proposed in this paper. First, the data is decomposed into several intrinsic mode functions (IMF) by variational mode decomposition (VMD). Then, the IMFs are reconstructed according to the sample entropy (SE). The lag order of historical carbon price is determined by Partial auto-correlation function (PACF), and the other pilot carbon price is selected according to pearson correlation (PC), both the chosen historical and other pilot carbon prices are used as the input of prediction model. Finally, extreme learning machine (ELM) optimized by whale optimization algorithm (WOA) is used to predict carbon price. In order to verify the validity of the model, the Hubei carbon price data are used for empirical analysis. The results show that the PC-VMD-WOA-ELM model proposed in this paper is effective and robust, and can predict carbon price more accurately.

关键词： Carbon price prediction Variational mode decomposition whale optimization algorithm Extreme learning machine Pearson correlation

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A variable-order fractional discrete grey model and its application

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期41卷 3509-3522页

作者： Huang Meixin Liu Caixia Sanjiang Univ Acad Affairs Off Nanjing Jiangsu Peoples R China Shanghai Inst Technol Sch Comp Sci & Informat Engineer Shanghai Peoples R China Zaozhuang Univ Dept Informat Sci & Engn Zaozhuang Shandong Peoples R China Jiangsu Normal Univ Coll Intelligent Educ Xuzhou Jiangsu Peoples R China

Fractional order grey model is effective in describing the uncertainty of the system. In this paper, we propose a novel variable-order fractional discrete grey model (short for VOFDGM(1,1)) by combining the discrete grey model and variable-order fractional accumulation, which is a more general form of the DGM(1,1). The detailed modeling procedure of the presented model is first systematically studied, in particular, matrix perturbation theory is used to prove the validity in terms of the stability of the model, and then, the model parameters are optimized by the whale optimization algorithm. The accuracy of the proposed model is verified by comparing it with classical models on six data sequences with different forms. Finally, the model is applied to predict the electricity consumption of Beijing and Liaoning Province of China, and the results show that the model has a better prediction performance compared with the other four commonly-used grey models. To the best of our knowledge, this is the first time that the variable-order fractional accumulation is introduced into the discrete grey model, which greatly increases the prediction accuracy of the DGM(1,1) and extends the application range of grey models.

关键词： Grey model variable-order fractional accumulation whale optimization algorithm electricity consumption

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A calibration method for infrared measurements on building facades based on a WOA-BP neural network

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INFRARED PHYSICS & TECHNOLOGY 2024年 137卷

作者： Feng, Guohui Pu, Yi Li, Huanyu Wang, Han Shenyang Jianzhu Univ Sch Municipal & Environm Engn Shenyang 110168 Peoples R China

As a critical method for analyzing the thermal properties of buildings, the audit results of infrared thermography are affected by the accuracy of temperature measurement. However, it is still a challenge to determine the actual temperature of the target surface due to various factors (e.g., object emissivity, ambient temperature, relative humidity, measurement distance, etc.). To this end, this paper proposes a robust temperature calibration model by utilizing a BP neural network trained with numerous sets of laboratory temperature measuring data, using the whale optimization algorithm (WOA) to optimize the initial network parameters, and rigorously validating the model with field measurements. The comparison of WOA-BP with traditional BP, SVM, RBF, PSO-BP and GWOBP confirms its superiority in terms of correction error and robustness, which can effectively solve the problems of temperature measurement deviations in building facades.

关键词： Infrared temperature calibration Building facades Neural network whale optimization algorithm

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Enhancing Economic Efficiency: Analyzing Transformer Life-Cycle Costs in Power Grids

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ENERGIES 2024年第3期17卷 606页

作者： Gui, Fangxu Chen, Heng Zhao, Xinyue Pan, Peiyuan Xin, Cheng Jiang, Xue North China Elect Power Univ Sch Energy Power & Mech Engn Beijing 102200 Peoples R China State Grid Econ & Tech Res Inst Co Beijing 102200 Peoples R China State Grid Liaoning Elect Power Co Econ & Tech Res Inst Shenyang 110000 Peoples R China

The transformer is a fundamental piece of equipment for power grids. The analysis and optimization of their life-cycle costs are of great importance to reinforce the economic efficiency of electrical networks. This paper constructs a comprehensive transformer life-cycle cost (LCC) model by fusing life-cycle cost theory with relevant transformer expenditure. It proceeds to examine the life-cycle cost aspects of the transformer, delving into its cost dynamics under various influencing factors, establishing interconnections between these factors and analyzing the cost relationship. By employing MATLAB software (Matlab 2021a) along with the whale optimization algorithm (WOA) this paper optimizes the objective function. Through this, it establishes the LCC model for 20 power transformers, obtaining the optimal objective function curve and the maximum value for LCC optimization of the transformer. Unlike previous research, this study adds a detailed analysis of several factors that influence LCC. At the same time, it develops a more complete, scientific and rational LCC optimization model. An illustrative example validates the model and the superiority of the whale optimization algorithm. The algorithm not only shows its scientific basis and superiority, but also serves as a guiding mechanism for LCC management in transformer engineering practices. Ultimately, it emerges as a fundamental tool to improve the efficiency of power grid asset management.

关键词： transformer life-cycle cost whale optimization algorithm cost proportion analysis cost impact factors

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Vehicle motion state estimation based on WOA-SVR

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2024年

作者： You, Yong Meng, Yunlong Wu, Jingtao Wang, Changqing Hebei Univ Technol Coll Mech Engn 8 Rd 1Dingzigu St Tianjin 300400 Peoples R China Tianjin Key Lab Power Transmiss & Safety Technol N Tianjin Peoples R China CATARC Automot Test Ctr Tianjin Co Ltd Tianjin Peoples R China

Obtaining accurate motion state information is the foundation of active movement control systems and advanced driving assistance system. To accurately obtain vehicle motion state information without the high-accuracy dynamic models parameters, this paper proposes a vehicle state estimation algorithm based on whale optimization algorithm-Support Vector Regression (WOA-SVR). A double-layer (longitudinal and lateral) regression estimation structure has been proposed based on analyzing the characteristic of vehicle dynamics. Then, the SVR model is trained on a dataset composed of multiple driving conditions, and the WOA is used to optimize the penalty factor c and kernel function parameter g in the relaxation variable during the training process;Finally, the estimation algorithm was validated through virtual simulation of single line change and frequency sweep tests, as well as ABS braking and double line change actual vehicle tests. The results show that this algorithm effectively improves estimation accuracy and is robust to changes in speed.

关键词： Vehicle state estimation dynamic model machine learning support vector regression whale optimization algorithm

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Optimizing fracture parameters in order to select based on theoretical concepts and concrete fracture energy prediction

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ENGINEERING FAILURE ANALYSIS 2024年 158卷

作者： Mirhosseini, Ramin Tabatabaei Aflatoonian, Moein Islamic Azad Univ Fac Engn Dept Civil Engn Kerman Branch Kerman Iran

Fracture energy is a critical factor in modeling the fracture behavior of quasi-brittle materials like concrete, a long-standing interest in the safety design of structures. Concrete exhibits sophisticated non-linear behavior during fracture and crack growth. The prediction accuracy of fracture energy using existing equations has always been of extensive interest to researchers. The current study has optimized the coefficients of a series of well-known approximate models using the whale optimization algorithm. This optimization allows for more accurate predictions of concrete's fracture energy, taking into account fracture parameters such as compressive strength, maximum aggregate size, and water-to-cement ratio. Compressive strength, maximum aggregate size, water-to-cement ratio, and aggregate type (river or crushed) were extracted and tabulated from reliable literature, forming a comprehensive database. An approximate average prediction formula was compiled from this data. Using 70% of the specimens from this database (374 samples) and the whale algorithm, the constant coefficients of models from previous studies were optimized. This optimization minimizes the difference between the laboratory-measured fracture energy and the model predictions. As a result, these optimized models offer a more reliable tool for estimating the fracture mechanism in concrete structures.

关键词： Fracture energy Fracture parameters Optimizing Dispersion Uncertainty Prediction Standards whale optimization algorithm

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Prediction of Vehicle Fuel Consumption Based on Combination of Improved whale optimization and XGBoost

Prediction of Vehicle Fuel Consumption Based on Combination ...

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第35届中国控制与决策会议

作者： Tonghui Jiang Ruiyou Zhang College of Information Science and Engineering Northeastern University

The highway transportation industry is the most oil-intensive industry in the ***,an accurate prediction of fuel consumption not only reveals the relationship between fuel consumption and driving characteristics,but also promotes green development of the automobile industry.A fuel consumption prediction model based on Extreme Gradient Boosting(XGBoost) is built in this research,where an improved whale optimization algorithm(IWOA) is developed to optimize the main parameters in the prediction *** the IWOA,Sobol sequence initialization is introduced to improve the diversity of the population and expand the search *** nonlinear convergence coefficient and Levy Flight are also introduced in the algorithm to balance the global exploration ability and the local exploitation *** experimental results show that the IWOA-XGBoost model performs better than some existing popular models in both accuracy and *** study provides fundamental theoretical,methodological,and technical support for automobile fuel saving and emission reduction.

关键词： Extreme Gradient Boosting whale optimization algorithm Sobol Sequence Levy Flight Fuel consumption prediction

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Enhancing sustainable urban economies: A statistical investigation of transitioning from fossil fuel-dependent industries to green innovation hubs

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SUSTAINABLE CITIES AND SOCIETY 2024年 101卷

作者： Liu, Fengyu Chen, Yuxiang Zhu, Jiulong Zhai, Ge Nanyang Inst Technol Fanli Business Sch Nanyang 473000 Peoples R China Zhongnan Univ Econ & Law Sch Finance Wuhan 430073 Peoples R China Zhejiang Univ Sch Publ Affairs Hangzhou 310058 Peoples R China Zhejiang Univ Inst Fiscal Big Data & Policy Hangzhou 310058 Peoples R China

As a result of the evolution of the smart grid, the new energy market has been restructured in urban systems, whereby domestic users play a more active role in the demand response (DR) programs with a view to balancing production and demand. DR incentive signals, though, are difficult to respond to due to limited understanding by users. As a result, an Energy Management Controller (EMC) has been expanded for responding to DR signals and solving energy management problems. The EMC employs a whale optimization algorithm (WOA) for automatically responding to the DR signal and solving energy management issues by planning 3 kinds of domestic load: non-interruptible, interruptible, and hybrid. With the WOA, the EMC generates the best home load plan for reducing power costs, carbon emissions, PARs, and customer dissatisfaction. Simulations were accomplished in order to evaluate the offered WOA against current algorithms such as particle swarm optimization (PSO), differential evaluation (DE), and teaching learn optimization algorithm (TLBO). The outcomes indicated that the suggested WOA reduces electricity costs, carbon emissions, PARs, and consumer dissatisfaction better than other algorithms.

关键词： whale optimization algorithm Demand response Electric vehicles Batteries Smart grid Renewable energy sources Scheduling

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Hybrid machine learning based energy policy and management in the renewable-based microgrids considering hybrid electric vehicle charging demand

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 128卷 106702-106702页

作者： Lei, Ming Mohammadi, Mojtaba Wuhan Univ Sci & Technol Sch Art & Design Wuhan 430030 Hubei Peoples R China Shiraz Univ Technol Dept Elect Engn Shiraz Iran

In this paper, a secured energy management architecture for both islanded and grid-connected operation modes of renewable hybrid AC-DC microgrids considering various renewable energy sources, distributed power generation units, energy storage, and plug-in hybrid electric vehicles (PHEV) is proposed. In this architecture, whale optimization algorithm (WOA) is employed to minimize the operation cost of the grid and also an intrusion detection system (IDS) based on the sequential hypothesis testing (SHT) approach is introduced to detect identity-based cyber-attacks (i.e. Sybil attack, masquerading attack) on the wireless-based advanced metering infrastructures (AMI). The proposed IDS makes use of the received signal strength (RSS) value to distinguish different signal sources and detect cyber-attacks. The feasibility and performance of the proposed architecture are tested on a practical hybrid microgrid, which is constructed based on the IEEE 33-bus test system. the results are provided for both islanded and grid-connected operation modes.

关键词： hybrid AC-DC microgrid Optimal energy management whale optimization algorithm Sequential hypothesis testing Security

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An Evolutionary Optimized Variational Mode Decomposition for Emotion Recognition

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IEEE SENSORS JOURNAL 2021年第2期21卷 2035-2042页

作者： Khare, Smith K. Bajaj, Varun Indian Inst Informat Technol Design & Mfg Elect & Commun Discipline Jabalpur 482005 India

Emotions provide valuable information regarding habits, health, mental activities, etc. Emotion recognition has attracted wide interests in affective computing, medical, brain-computer interface, and other relevant fields. Emotion recognition from EEG signals has facilitated to impaired peoples to intact with real world. Multi-components EEG signal need to decomposed into set of components to extract the hidden information, empirical selection of decomposition parameters can lead to loss of information due to mode mixing, generation of noisy modes, poor signal synthesis, etc. In this paper, optimized variational mode decomposition is proposed for emotion recognition using single-channel EEG signals. The Eigenvector centrality method (EVCM) is employed for dominant channel selection. An optimum number of modes (K-opt) and penalty factor (alpha(opt)) are selected adaptively for decomposition of non- stationary EEG signals. Time-domain features are elicited from the modes of EEG signals. The posthoc analysis are employed for significant features selection, these features are used as input to different classifiers for emotion classification. The overall accuracy of 97.24% is achieved with an extreme learning machine classifier for a four emotion classification. The performance of the proposed method is evaluated by five performance parameters. F-1 score, false-positive rate, Mathew's correlation coefficient, and Cohen's Kappa of 0.9454, 0.94%, 92.92%, and 0.9633 is obtained. In comparison with traditional variationalmode decomposition and existing state-of-the-art using the same dataset, the proposed method shows improved performance of about 4% and 2%.

关键词： Electroencephalography Feature extraction Emotion recognition Support vector machines Time-frequency analysis Brain modeling Wavelet analysis Optimized variational mode decomposition whale optimization algorithm eigenvector centrality method emotions recognition

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