Enhancing Port Energy Autonomy Through Hybrid Renewables and Optimized Energy Storage Management by Dimitrios Cholidis, Nikolaos Sifakis, Nikolaos Savvakis, George Tsinarakis, Avraam Kartalidis, George Arampatzis

“…Hybrid renewable energy systems (HRESs) are being incorporated and evaluated within seaports to realize efficiencies, reduce dependence on grid electricity, and reduce operating costs. The paper adopts a genetic algorithm (GA)-based optimization framework to assess four energy management scenarios that embed wind turbines (WTs), photovoltaic energy (PV), an energy storage system (ESS), and an energy management system (EMS). …”
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Thermodynamic optimization of a multi-generation system integrating gas turbine-helium reactor cycle with hydrogen production and desalination by Yang Wang, Yuliang Chen, Zhen Su

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Robust Optimization for Microgrid Management With Compensator, EV, Storage, Demand Response, and Renewable Integration by Hamid Hematian, Mohamad Tolou Askari, Meysam Amir Ahmadi, Mahmood Sameemoqadam, Majid Babaei Nik

“…Numerical simulations on a test system validate the effectiveness of the proposed model and solution algorithm, showing a significant reduction in the operating costs of the microgrid. …”
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Optimization Study of PVT Coupled Water Loop Heat Pump Energy Supply System by Lv Tiangang, Liu Bing, Wu Jun, Zhu Li, Hou Jingxuan

“…This study designs a building energy supply system integrating PVT and a water-loop heat pump based on Tianjin's meteorological data, using TRNSYS software to establish a dynamic model. By employing the Hooke-Jeeves algorithm, the system's design and operation parameters were optimized with annual cost and system COP as objective functions. …”
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Optimal Activity Recognition Framework Based on Improvement of Regularized Neighborhood Component Analysis (RNCA) by Norazman Shahar, Muhammad Amir As'Ari, Tan Tian Swee, Nurul Fathia Ghazali

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Short-term load estimation based on improved DBN-LSTM by Nan Dong, Yuwen Wu, Buyun Su, Zhanzhi Liu

“…The pruning algorithm is used to optimize the redundant structure of the model, reduce the complexity and training time of the model, and maintain or improve the forecasting accuracy. …”
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Optimization method of investment package based on Markowitz portfolio theory by A. D. Baydalin

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Gaussian Process Regression Total Nitrogen Prediction Based on Data Decomposition Technology and Several Intelligent Algorithms by WANG Yongshun, CUI Dongwen

“…Total nitrogen (TN) is one of the important indicators to reflect the degree of water pollution and measure the eutrophication status of lakes and reservoirs.To improve the accuracy of TN prediction,based on the empirical wavelet transform (EWT) and wavelet packet transform (WPT) decomposition technology,this paper proposes a Gaussian process regression (GPR) prediction model optimized by osprey optimization algorithm (OOA),rime optimization algorithm (ROA),bald eagle search (BES) and black widow optimization algorithm (BWOA) respectively.Firstly,the TN time series is decomposed into several more regular subsequence components by EWT and WPT respectively.Then,the paper briefly introduces the principles of OOA,ROA,BES,and BWOA algorithms and applies OOA,ROA,BES,and BWOA to optimize GPR hyperparameters.Finally,EWT-OOA-GPR,EWT-ROA-GPR,EWT-BES-GPR,EWT-BWOA-GPR,WPT-OOA-GPR,WPT-ROA-GPR,WPT-BES-GPR,WPT-BWOA-GPR models (EWT-OOA-GPR and other eight models for short) are established to predict the components of TN by the optimized super-parameters.The final prediction results are obtained after reconstruction,and WT-OOA-GPR,WT-ROA-GPR,WT-BES-GPR and WT-BWOA-GPR models based on wavelet transform (WT) are built.Eight models,including EWT-OOA-SVM based on support vector machine (SVM),the paper compares the unoptimized EWT-GPR,WPT-GPR models,and the uncomposed OOA-GPR,ROA-GPR,BES-GPR,and BWOA-GPR models.The models were verified by the monitoring TN concentration time series data of Mudihe Reservoir,an important drinking water source in China,from 2008 to 2022.The results are as follows.① The average absolute percentage error of eight models such as EWT-OOA-GPR for TN prediction is between 0.161% and 0.219%,and the coefficient of determination is 0.999 9,which is superior to other comparison models,with higher prediction accuracy and better generalization ability.② EWT takes into account the advantages of WT and EMD.WPT can decompose low-frequency and high-frequency signals at the same time.Both of them can decompose TN time series data into more regular modal components,significantly improving the accuracy of model prediction,and the decomposition effect is better than that of the WT method.③ OOA,ROA,BES,and BWOA can effectively optimize GPR hyperparameters and improve GPR prediction performance.…”
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Optimized solar PV integration for voltage enhancement and loss reduction in the Kombolcha distribution system using hybrid grey wolf-particle swarm optimization by Awot Getachew Abera, Tefera Terefe Yetayew, Assen Beshr Alyu

“…A hybrid optimization approach combining Particle Swarm Optimization and Grey Wolf Optimization algorithms is proposed for determining optimal sizing and placement of PV-based DGs. …”
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An Enhanced Forwarding Method based on Intelligent Water Drops Algorithm in Named Data Network by HamidReza Afzal, Behrang Barekatain, Zahra Beheshti

“…Although Named Data Network (NDN) has made a bright future in Internet for high volume of requests by many users, how to send a request package (I-Pkt) consciously from the consumer to the Producer and returning the data package (D-Pkt) inversely is still one of its most important challenges. According to the recent limited researches, using service quality parameters beside an optimization algorithm like ant colony to find the optimal path has been an appropriate response to solve this problem. …”
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Conditional distributionally robust dispatch for integrated transmission-distribution systems via distributed optimization by Jie Li, Xiuli Wang, Zhicheng Wang, Zhenzi Song

“…This paper closes this gap by proposing a conditional distributionally robust optimization (DRO) method for ITDSs. Specifically, a novel ambiguity set is built by exploiting the dependence of the wind power forecast error on its forecast value, which differs from most of the existing ones. …”
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An Assessment of High-Order-Mode Analysis and Shape Optimization of Expansion Chamber Mufflers by Min-Chie CHIU, Ying-Chun CHANG

“…Using an eigenfunction (higher-order-mode analysis), a four-pole system matrix for evaluating acoustic performance (STL) is derived. To improve the acoustic performance of the expansion chamber muffler, three kinds of expansion chamber mufflers (KA-KC) with different acoustic mechanisms are introduced and optimized for a targeted tone using a genetic algorithm (GA). …”
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Multi-Objective Optimization of Insulation Thickness with Respect to On-Site RES Generation in Residential Buildings by Agis M. Papadopoulos, Konstantinos Polychronakis, Elli Kyriaki, Effrosyni Giama

“…This paper investigates the optimization of insulation thickness with respect to the integration of renewable energy systems in residential buildings in order to improve energy efficiency, maximize the contribution of renewables and reduce life cycle costs. …”
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Optimizing Q-Learning for Automated Cavity Filter Tuning: Leveraging PCA and Neural Networks by Aghanim Amina, Otman Oulhaj, Oukaira Aziz, Lasri Rafik

“…This paper presents a reinforcement learning-based approach to automate the tuning of a 6thorder combline bandpass filter, operating at 941 MHz, using a Q-learning algorithm. To reduce complexity, only two tuning screws are considered in the optimization. …”
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Joint Configuration and Optimization of Multi-microgrid Shared Energy Storage Based on Coalition Game by Congwei JIANG, Qinghe OU, Zhongchao WU, Jian ZHANG, Shu YANG, Jianan ZHU, Qian AI

“…Considering that the capacity configuration and operation optimization of energy storage equipment are coupled, this paper adopts an energy cell–organization framework, proposes a two-stage multi-objective optimal configuration scheme of the shared energy storage based on a coalition game, and fairly allocates the coalition cooperation costs by using the Shapley method. …”
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Optimization of Graphene Oxide’s Characteristics with TOPSIS Using an Automated Decision-Making Process by Javanbakht T.

“…Moreover, their advantages and inconveniences could be investigated better once this investigation provides information on optimizing its candidates. In the current research work, a novel automated decision-making process was used with the TOPSIS algorithm using the Łukasiewicz disjunction, which helped detect the confusion of properties and determine its impact on the rank of candidates. …”
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Multi objective optimization and experimental investigation of the stirring performance of a novel micro actuator by Zhuowei He, Junjie Lei, Jingjing Yang, Huba Zhu

“…Subsequently, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is utilized for Multi-Objective Optimization to identify the optimal combination of structural parameters. …”
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Data-Driven Battery Remaining Life Prediction Based on ResNet with GA Optimization by Jixiang Zhou, Weijian Huang, Haiyan Dai, Chuang Wang, Yuhua Zhong

“…To this end, this paper proposes a data-driven lithium-ion battery life prediction method based on residual network (ResNet) and genetic algorithm (GA) optimization, which is designed to screen the features of the lithium-ion battery training data in order to effectively reduce the redundant features and improve the prediction performance of the model. …”
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Bionic Compass Method Based on Atmospheric Polarization Optimization in Non-Ideal Clear Condition by Yuyang Li, Xia Wang, Min Zhang, Ruiqiang Li, Qiyang Sun

“…This paper proposes a bionic navigation method based on atmospheric polarization optimization to improve heading accuracy under non-ideal clear conditions. …”
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Research on improving the ranging accuracy of ships with stereo vision through Kalman filter optimization. by Zhongbo Peng, Jie Han, Liang Tong, Lumeng Wang, Dan Liu, Shijie Sun

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