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  • Open Access

    ARTICLE

    Dynamic Economic Scheduling with Self-Adaptive Uncertainty in Distribution Network Based on Deep Reinforcement Learning

    Guanfu Wang1, Yudie Sun1, Jinling Li2,3,*, Yu Jiang1, Chunhui Li1, Huanan Yu2,3, He Wang2,3, Shiqiang Li2,3

    Energy Engineering, Vol.121, No.6, pp. 1671-1695, 2024, DOI:10.32604/ee.2024.047794

    Abstract Traditional optimal scheduling methods are limited to accurate physical models and parameter settings, which are difficult to adapt to the uncertainty of source and load, and there are problems such as the inability to make dynamic decisions continuously. This paper proposed a dynamic economic scheduling method for distribution networks based on deep reinforcement learning. Firstly, the economic scheduling model of the new energy distribution network is established considering the action characteristics of micro-gas turbines, and the dynamic scheduling model based on deep reinforcement learning is constructed for the new energy distribution network system with a More >

  • Open Access

    ARTICLE

    Transient Stability Preventive Control of Wind Farm Connected Power System Considering the Uncertainty

    Yuping Bian*, Xiu Wan, Xiaoyu Zhou

    Energy Engineering, Vol.121, No.6, pp. 1637-1656, 2024, DOI:10.32604/ee.2024.047678

    Abstract To address uncertainty as well as transient stability constraints simultaneously in the preventive control of wind farm systems, a novel three-stage optimization strategy is established in this paper. In the first stage, the probabilistic multi-objective particle swarm optimization based on the point estimate method is employed to cope with the stochastic factors. The transient security region of the system is accurately ensured by the interior point method in the second stage. Finally, the verification of the final optimal objectives and satisfied constraints are enforced in the last stage. Furthermore, the proposed strategy is a general More >

  • Open Access

    ARTICLE

    Generalized nth-Order Perturbation Method Based on Loop Subdivision Surface Boundary Element Method for Three-Dimensional Broadband Structural Acoustic Uncertainty Analysis

    Ruijin Huo1,2,3, Qingxiang Pei1,2,3, Xiaohui Yuan1,*, Yanming Xu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 2053-2077, 2024, DOI:10.32604/cmes.2024.049185

    Abstract In this paper, a generalized th-order perturbation method based on the isogeometric boundary element method is proposed for the uncertainty analysis of broadband structural acoustic scattering problems. The Burton-Miller method is employed to solve the problem of non-unique solutions that may be encountered in the external acoustic field, and the th-order discretization formulation of the boundary integral equation is derived. In addition, the computation of loop subdivision surfaces and the subdivision rules are introduced. In order to confirm the effectiveness of the algorithm, the computed results are contrasted and analyzed with the results under Monte More >

  • Open Access

    ARTICLE

    Provoking Buying Behaviors Amid Crises: Unfolding the Underlying Mechanisms of Psychological Impairments

    Muhammad Waleed Ayub Ghouri1, Guofeng Wang2, Muhammad Ali Hussain3, Zhisheng Li1,*, Tachia Chin1

    International Journal of Mental Health Promotion, Vol.26, No.4, pp. 279-292, 2024, DOI:10.32604/ijmhp.2024.044759

    Abstract Crises in the past have caused devastating, long-lasting impacts on the global economy. The after-effects always bring some dynamic and rigorous challenges for businesses and governments. Such challenges have always been a point of discussion for scholars. The recent COVID-19 pandemic emaciated the global economy, leaving everyone mired in uncertainty, fear, and psychological impairments. One of the headwind features utilized by consumers during pandemic was panic buying (PB), which must be explored in various contexts for policymakers and practitioners. To address this gap, this study deployed a moderated mediation mechanism, integrating the health belief model… More >

  • Open Access

    ARTICLE

    Combined CNN-LSTM Deep Learning Algorithms for Recognizing Human Physical Activities in Large and Distributed Manners: A Recommendation System

    Ameni Ellouze1, Nesrine Kadri2, Alaa Alaerjan3,*, Mohamed Ksantini1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 351-372, 2024, DOI:10.32604/cmc.2024.048061

    Abstract Recognizing human activity (HAR) from data in a smartphone sensor plays an important role in the field of health to prevent chronic diseases. Daily and weekly physical activities are recorded on the smartphone and tell the user whether he is moving well or not. Typically, smartphones and their associated sensing devices operate in distributed and unstable environments. Therefore, collecting their data and extracting useful information is a significant challenge. In this context, the aim of this paper is twofold: The first is to analyze human behavior based on the recognition of physical activities. Using the… More >

  • Open Access

    ARTICLE

    Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

    Haojie Lian1, Jiaqi Wang1, Leilei Chen2,*, Shengze Li3, Ruochen Cao4, Qingyuan Hu5, Peiyun Zhao1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 1143-1163, 2024, DOI:10.32604/cmes.2024.048549

    Abstract This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke from 2D images. This approach reconstructs color and density fields from 2D images using Neural Radiance Field (NeRF) and improves image quality using frequency regularization. The NeRF model is obtained via joint training of multiple artificial neural networks, whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each pixel. In addition, customized physics-informed neural network (PINN) with residual blocks and two-layer activation functions are utilized to input the density fields of More >

  • Open Access

    ARTICLE

    Optimal Bidding Strategies of Microgrid with Demand Side Management for Economic Emission Dispatch Incorporating Uncertainty and Outage of Renewable Energy Sources

    Mousumi Basu1, Chitralekha Jena2, Baseem Khan3,4,*, Ahmed Ali4

    Energy Engineering, Vol.121, No.4, pp. 849-867, 2024, DOI:10.32604/ee.2024.043294

    Abstract In the restructured electricity market, microgrid (MG), with the incorporation of smart grid technologies, distributed energy resources (DERs), a pumped-storage-hydraulic (PSH) unit, and a demand response program (DRP), is a smarter and more reliable electricity provider. DER consists of gas turbines and renewable energy sources such as photovoltaic systems and wind turbines. Better bidding strategies, prepared by MG operators, decrease the electricity cost and emissions from upstream grid and conventional and renewable energy sources (RES). But it is inefficient due to the very high sporadic characteristics of RES and the very high outage rate. To More >

  • Open Access

    ARTICLE

    ON UNCERTAINTY AND LOCAL SENSITIVITY ANALYSIS FOR STEADY-STATE CONJUGATE HEAT TRANSFER PROBLEMS PART 1: EMISSIVITY, FLUID TEMPERATURE, AND CONDUCTANCE

    Christian Rauch*

    Frontiers in Heat and Mass Transfer, Vol.2, No.3, pp. 1-8, 2011, DOI:10.5098/hmt.v2.3.3006

    Abstract In recent years, significant effort has been placed into developing automated multi-physics simulation. The exchange of boundary conditions has lead to more realistic as well as more complex simulations with usually slower convergence rate when the coupling is being performed between two different codes. In this paper the equations of local sensitivities for element centered steady-state combined convection, conduction, and thermal radiation problems are being derived. A numerical analysis on the stability of the solution matrix is being conducted. Partial uncertainties and the relative importance of the heat transfer modes are investigated by their uncertainty More >

  • Open Access

    ARTICLE

    Research on Regulation Method of Energy Storage System Based on Multi-Stage Robust Optimization

    Zaihe Yang1,*, Shuling Wang1, Runhang Zhu1, Jiao Cui2, Ji Su2, Liling Chen3

    Energy Engineering, Vol.121, No.3, pp. 807-820, 2024, DOI:10.32604/ee.2023.028167

    Abstract To address the scheduling problem involving energy storage systems and uncertain energy, we propose a method based on multi-stage robust optimization. This approach aims to regulate the energy storage system by using a multi-stage robust optimal control method, which helps overcome the limitations of traditional methods in terms of time scale. The goal is to effectively utilize the energy storage power station system to address issues caused by unpredictable variations in environmental energy and fluctuating load throughout the day. To achieve this, a mathematical model is constructed to represent uncertain energy sources such as photovoltaic More >

  • Open Access

    ARTICLE

    An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

    Abidhan Bardhan1,*, Raushan Kumar Singh2, Mohammed Alatiyyah3, Sulaiman Abdullah Alateyah4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1521-1555, 2024, DOI:10.32604/cmes.2023.044467

    Abstract This research proposes a highly effective soft computing paradigm for estimating the compressive strength (CS) of metakaolin-contained cemented materials. The proposed approach is a combination of an enhanced grey wolf optimizer (EGWO) and an extreme learning machine (ELM). EGWO is an augmented form of the classic grey wolf optimizer (GWO). Compared to standard GWO, EGWO has a better hunting mechanism and produces an optimal performance. The EGWO was used to optimize the ELM structure and a hybrid model, ELM-EGWO, was built. To train and validate the proposed ELM-EGWO model, a sum of 361 experimental results… More >

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