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  • Open Access

    ARTICLE

    Adaptive Multi-Objective Energy Management Strategy Considering the Differentiated Demands of Distribution Networks with a High Proportion of New-Generation Sources and Loads

    Huang Tan1, Haibo Yu1, Tianyang Chen1, Hanjun Deng2, Yetong Hu3,*

    Energy Engineering, Vol.122, No.5, pp. 1949-1973, 2025, DOI:10.32604/ee.2025.062574 - 25 April 2025

    Abstract With the increasing integration of emerging source-load types such as distributed photovoltaics, electric vehicles, and energy storage into distribution networks, the operational characteristics of these networks have evolved from traditional single-load centers to complex multi-source, multi-load systems. This transition not only increases the difficulty of effectively classifying distribution networks due to their heightened complexity but also renders traditional energy management approaches—primarily focused on economic objectives—insufficient to meet the growing demands for flexible scheduling and dynamic response. To address these challenges, this paper proposes an adaptive multi-objective energy management strategy that accounts for the distinct operational… More >

  • Open Access

    ARTICLE

    A Two-Stage Feature Extraction Approach for Green Energy Consumers in Retail Electricity Markets Using Clustering and TF–IDF Algorithms

    Wei Yang1, Weicong Tan1, Zhijian Zeng1, Ren Li1, Jie Qin1, Yuting Xie1, Yongjun Zhang2, Runting Cheng2, Dongliang Xiao2,*

    Energy Engineering, Vol.122, No.5, pp. 1697-1713, 2025, DOI:10.32604/ee.2025.060571 - 25 April 2025

    Abstract The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies, which poses greater challenges for the market service for green energy consumers. This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and term frequency-inverse document frequency (TF–IDF) algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market. First, the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and… More >

  • Open Access

    ARTICLE

    Ordered Clustering-Based Semantic Music Recommender System Using Deep Learning Selection

    Weitao Ha1, Sheng Gang2, Yahya D. Navaei3, Abubakar S. Gezawa4, Yaser A. Nanehkaran2,5,*

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3025-3057, 2025, DOI:10.32604/cmc.2025.061343 - 16 April 2025

    Abstract Music recommendation systems are essential due to the vast amount of music available on streaming platforms, which can overwhelm users trying to find new tracks that match their preferences. These systems analyze users’ emotional responses, listening habits, and personal preferences to provide personalized suggestions. A significant challenge they face is the “cold start” problem, where new users have no past interactions to guide recommendations. To improve user experience, these systems aim to effectively recommend music even to such users by considering their listening behavior and music popularity. This paper introduces a novel music recommendation system… More >

  • Open Access

    ARTICLE

    Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

    Aadil Mushtaq Pandith1, Manni Kumar2, Naveen Kumar3, Nitin Goyal4,*, Sachin Ahuja2, Yonis Gulzar5, Rashi Rastogi6, Rupesh Gupta7

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 259-279, 2025, DOI:10.32604/cmc.2025.062608 - 26 March 2025

    Abstract Underwater wireless sensor networks (UWSNs) rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the sink. However, many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater environments. Additionally, conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink, commonly known as the energy hole issue. Moreover, cluster-based aggregation methods face significant challenges such as cluster head (CH) failures and collisions within clusters that degrade overall network performance. To address these limitations,… More >

  • Open Access

    ARTICLE

    FedCPS: A Dual Optimization Model for Federated Learning Based on Clustering and Personalization Strategy

    Zhen Yang1, Yifan Liu1,2,*, Fan Feng3, Yi Liu3, Zhenpeng Liu1,3

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 357-380, 2025, DOI:10.32604/cmc.2025.060709 - 26 March 2025

    Abstract Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’ devices without sharing private data. It trains a global model through collaboration between clients and the server. However, the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability. Meanwhile, data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks, and standalone personalization tasks may cause severe overfitting issues. To address these limitations, we introduce a federated learning dual optimization model based on… More >

  • Open Access

    ARTICLE

    Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

    Jeng-Shyang Pan1,2,3, Mengfei Zhang1, Shu-Chuan Chu2,*, Xingsi Xue4, Václav Snášel5

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 475-496, 2025, DOI:10.32604/cmc.2025.060170 - 26 March 2025

    Abstract Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data analysis. Traditional clustering algorithms, such as K-means, are widely used due to their simplicity and efficiency. This paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm (SPPE) to improve clustering performance. The SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution (PPE) algorithm. Firstly, a Variable Neighborhood Search (VNS) factor is incorporated to strengthen the local search capability and foster population diversity. Secondly, a position update model, incorporating a spiral mechanism, is… More >

  • Open Access

    ARTICLE

    Multi-Order Neighborhood Fusion Based Multi-View Deep Subspace Clustering

    Kai Zhou1, Yanan Bai2, Yongli Hu3, Boyue Wang3,*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3873-3890, 2025, DOI:10.32604/cmc.2025.060918 - 06 March 2025

    Abstract Existing multi-view deep subspace clustering methods aim to learn a unified representation from multi-view data, while the learned representation is difficult to maintain the underlying structure hidden in the origin samples, especially the high-order neighbor relationship between samples. To overcome the above challenges, this paper proposes a novel multi-order neighborhood fusion based multi-view deep subspace clustering model. We creatively integrate the multi-order proximity graph structures of different views into the self-expressive layer by a multi-order neighborhood fusion module. By this design, the multi-order Laplacian matrix supervises the learning of the view-consistent self-representation affinity matrix; then, More >

  • Open Access

    ARTICLE

    kProtoClust: Towards Adaptive k-Prototype Clustering without Known k

    Yuan Ping1,2,*, Huina Li1, Chun Guo3, Bin Hao4

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4949-4976, 2025, DOI:10.32604/cmc.2025.057693 - 06 March 2025

    Abstract Towards optimal k-prototype discovery, k-means-like algorithms give us inspirations of central samples collection, yet the unstable seed samples selection, the hypothesis of a circle-like pattern, and the unknown K are still challenges, particularly for non-predetermined data patterns. We propose an adaptive k-prototype clustering method (kProtoClust) which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and merging. On behalf of a group of data samples, support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes, while inner samples become the first candidates for… More >

  • Open Access

    ARTICLE

    SGP-GCN: A Spatial-Geological Perception Graph Convolutional Neural Network for Long-Term Petroleum Production Forecasting

    Xin Liu1,*, Meng Sun1, Bo Lin2, Shibo Gu1

    Energy Engineering, Vol.122, No.3, pp. 1053-1072, 2025, DOI:10.32604/ee.2025.060489 - 07 March 2025

    Abstract Long-term petroleum production forecasting is essential for the effective development and management of oilfields. Due to its ability to extract complex patterns, deep learning has gained popularity for production forecasting. However, existing deep learning models frequently overlook the selective utilization of information from other production wells, resulting in suboptimal performance in long-term production forecasting across multiple wells. To achieve accurate long-term petroleum production forecast, we propose a spatial-geological perception graph convolutional neural network (SGP-GCN) that accounts for the temporal, spatial, and geological dependencies inherent in petroleum production. Utilizing the attention mechanism, the SGP-GCN effectively captures… More >

  • Open Access

    ARTICLE

    Reactive Power Optimization Model of Active Distribution Network with New Energy and Electric Vehicles

    Chenxu Wang*, Jing Bian, Rui Yuan

    Energy Engineering, Vol.122, No.3, pp. 985-1003, 2025, DOI:10.32604/ee.2025.059559 - 07 March 2025

    Abstract Considering the uncertainty of grid connection of electric vehicle charging stations and the uncertainty of new energy and residential electricity load, a spatio-temporal decoupling strategy of dynamic reactive power optimization based on clustering-local relaxation-correction is proposed. Firstly, the k-medoids clustering algorithm is used to divide the reduced power scene into periods. Then, the discrete variables and continuous variables are optimized in the same period of time. Finally, the number of input groups of parallel capacitor banks (CB) in multiple periods is fixed, and then the secondary static reactive power optimization correction is carried out by… More >

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