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  • Open Access

    ARTICLE

    DecMamba: Mamba Utilizing Series Decomposition for Multivariate Time Series Forecasting

    Jianxin Feng*, Jianhao Zhang, Ge Cao, Zhiguo Liu, Yuanming Ding

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1049-1068, 2025, DOI:10.32604/cmc.2024.058374 - 03 January 2025

    Abstract Multivariate time series forecasting is widely used in traffic planning, weather forecasting, and energy consumption. Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series. However, the decomposition kernel of previous decomposition-based models is fixed, and these models have not considered the differences in frequency fluctuations between components. These problems make it difficult to analyze the intricate temporal variations of real-world time series. In this paper, we propose a series decomposition-based Mamba model, DecMamba, to obtain the intricate temporal dependencies and… More >

  • Open Access

    ARTICLE

    A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

    Li Ma1, Cai Dai1,*, Xingsi Xue2, Cheng Peng3

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 997-1026, 2025, DOI:10.32604/cmc.2024.057168 - 03 January 2025

    Abstract The multi-objective particle swarm optimization algorithm (MOPSO) is widely used to solve multi-objective optimization problems. In the article, a multi-objective particle swarm optimization algorithm based on decomposition and multi-selection strategy is proposed to improve the search efficiency. First, two update strategies based on decomposition are used to update the evolving population and external archive, respectively. Second, a multi-selection strategy is designed. The first strategy is for the subspace without a non-dominated solution. Among the neighbor particles, the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle… More >

  • Open Access

    ARTICLE

    Recent Advancements in the Optimization Capacity Configuration and Coordination Operation Strategy of Wind-Solar Hybrid Storage System

    Hongliang Hao1, Caifeng Wen2,3, Feifei Xue2,*, Hao Qiu1, Ning Yang2, Yuwen Zhang1, Chaoyu Wang1, Edwin E. Nyakilla1

    Energy Engineering, Vol.122, No.1, pp. 285-306, 2025, DOI:10.32604/ee.2024.057720 - 27 December 2024

    Abstract Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources. This paper proposes a wind-solar hybrid energy storage system (HESS) to ensure a stable supply grid for a longer period. A multi-objective genetic algorithm (MOGA) and state of charge (SOC) region division for the batteries are introduced to solve the objective function and configuration of the system capacity, respectively. MATLAB/Simulink was used for simulation test. The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system, with a combination of a 300 More >

  • Open Access

    ARTICLE

    Joint Estimation of SOH and RUL for Lithium-Ion Batteries Based on Improved Twin Support Vector Machineh

    Liyao Yang1, Hongyan Ma1,2,3,*, Yingda Zhang1, Wei He1

    Energy Engineering, Vol.122, No.1, pp. 243-264, 2025, DOI:10.32604/ee.2024.057500 - 27 December 2024

    Abstract Accurately estimating the State of Health (SOH) and Remaining Useful Life (RUL) of lithium-ion batteries (LIBs) is crucial for the continuous and stable operation of battery management systems. However, due to the complex internal chemical systems of LIBs and the nonlinear degradation of their performance, direct measurement of SOH and RUL is challenging. To address these issues, the Twin Support Vector Machine (TWSVM) method is proposed to predict SOH and RUL. Initially, the constant current charging time of the lithium battery is extracted as a health indicator (HI), decomposed using Variational Modal Decomposition (VMD), and… More >

  • Open Access

    ARTICLE

    Coordinated Control Strategy of New Energy Power Generation System with Hybrid Energy Storage Unit

    Yun Zhang1,*, Zifen Han2, Biao Tian1, Ning Chen2, Yi Fan3

    Energy Engineering, Vol.122, No.1, pp. 167-184, 2025, DOI:10.32604/ee.2024.056190 - 27 December 2024

    Abstract The new energy power generation is becoming increasingly important in the power system. Such as photovoltaic power generation has become a research hotspot, however, due to the characteristics of light radiation changes, photovoltaic power generation is unstable and random, resulting in a low utilization rate and directly affecting the stability of the power grid. To solve this problem, this paper proposes a coordinated control strategy for a new energy power generation system with a hybrid energy storage unit based on the lithium iron phosphate-supercapacitor hybrid energy storage unit. Firstly, the variational mode decomposition algorithm is… More >

  • Open Access

    ARTICLE

    A Combined Method of Temporal Convolutional Mechanism and Wavelet Decomposition for State Estimation of Photovoltaic Power Plants

    Shaoxiong Wu1, Ruoxin Li1, Xiaofeng Tao1, Hailong Wu1,*, Ping Miao1, Yang Lu1, Yanyan Lu1, Qi Liu2, Li Pan2

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

    Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

  • Open Access

    ARTICLE

    Seasonal Short-Term Load Forecasting for Power Systems Based on Modal Decomposition and Feature-Fusion Multi-Algorithm Hybrid Neural Network Model

    Jiachang Liu1,*, Zhengwei Huang2, Junfeng Xiang1, Lu Liu1, Manlin Hu1

    Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

    Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

  • Open Access

    ARTICLE

    Short-Term Wind Power Prediction Based on WVMD and Spatio-Temporal Dual-Stream Network

    Yingnan Zhao*, Yuyuan Ruan, Zhen Peng

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 549-566, 2024, DOI:10.32604/cmc.2024.056240 - 15 October 2024

    Abstract As the penetration ratio of wind power in active distribution networks continues to increase, the system exhibits some characteristics such as randomness and volatility. Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control. Based on the spatio-temporal features of Numerical Weather Prediction (NWP) data, it proposes the WVMD_DSN (Whale Optimization Algorithm, Variational Mode Decomposition, Dual Stream Network) model. The model first applies Pearson correlation coefficient (PCC) to choose some NWP features with strong correlation to wind power to form the feature set. Then, it decomposes the feature set More >

  • Open Access

    PROCEEDINGS

    Solving Advection-Diffusion Equation by Proper Generalized Decomposition with Coordinate Transformation

    Xinyi Guan1, Shaoqiang Tang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.010869

    Abstract Inheriting a convergence difficulty explained by the Kolmogorov N-width [1], the advection-diffusion equation is not effectively solved by the Proper Generalized Decomposition [2] (PGD) method. In this paper, we propose a new strategy: Proper Generalized Decomposition with Coordinate Transformation (CT-PGD). Converting the mixed hyperbolic-parabolic equation to a parabolic one, it resumes the efficiency of convergence for advection-dominant problems. Combining PGD with CT-PGD, we solve advection-diffusion equation by much fewer degrees of freedom, hence improve the efficiency. The advection-dominant regime and diffusion-dominant regime are quantitatively classified by a threshold, computed numerically. Moreover, we find that appropriate More >

  • Open Access

    ARTICLE

    A Microseismic Signal Denoising Algorithm Combining VMD and Wavelet Threshold Denoising Optimized by BWOA

    Dijun Rao1,2,3,4, Min Huang1,2,3,5, Xiuzhi Shi4, Zhi Yu6,*, Zhengxiang He7

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 187-217, 2024, DOI:10.32604/cmes.2024.051402 - 20 August 2024

    Abstract The denoising of microseismic signals is a prerequisite for subsequent analysis and research. In this research, a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm (BWOA) optimized Variational Mode Decomposition (VMD) joint Wavelet Threshold Denoising (WTD) algorithm (BVW) is proposed. The BVW algorithm integrates VMD and WTD, both of which are optimized by BWOA. Specifically, this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited Intrinsic Mode Functions (BLIMFs). Subsequently, these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold… More >

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