Multi-Time Scale Coordinated Optimization of Energy Systems Under Flexible Load Response by Jinfeng Gao, Daifeng Gao, Chun Xiao

“…To address these challenges, this study investigates multi-time scale collaborative optimization of energy systems based on flexible load response, utilizing a combination of qualitative and quantitative methods. …”
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Search direction optimization of power flow analysis based on physics-informed deep learning by Baoliang Li, Qiuwei Wu, Yongji Cao, Changgang Li

“…Power flow analysis is crucial for obtaining power system operation states and optimizing control measures. The increasing integration of renewable energy sources has resulted in a more complex power system, posing challenges to the computational efficiency and convergence of conventional power analysis methods. …”
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Value chain optimization in large scale gas network considering elevation and transmission direction by Xifeng Ning, Jinfeng Qiu, Dejun Yu

“…Numeric experiments demonstrate the method’s efficiency in generating high-quality solutions for the optimization problem.…”
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A GD-PSO Algorithm for Smart Transportation Supply Chain ABS Portfolio Optimization by Yingjia Sun, Hongfeng Ren

“…Different from forward selection or linear optimization, which could have low efficiency for complicated problems with large sample size and multiple objectives, new methods and algorithms for NP-hard problems would be necessary to be investigated. …”
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Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations by Panagiotis Michailidis, Iakovos Michailidis, Elias Kosmatopoulos

“…One significant branch of modern control algorithms concerns reinforcement learning, a data-driven strategy capable of dynamically managing renewable energy sources and other energy subsystems under uncertainty and real-time constraints. …”
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Designing the optimal number of active branches in a multi-branch buck-boost converter by I. Kovacova, D. Kovac

“…The novelty of the proposed work consists in the development of a precise method for determining the optimal number of branches in a multi-branch buck-boost converter for a specified output power. …”
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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

“…The model primarily addresses challenges arising from the integration of power electronics-based generation units, the unpredictable nature of demand in microgrids, and the integration of small-scale renewable energy sources. The proposed model includes detailed formulations for MG energy management, covering optimal battery usage, efficient EV energy management, compensator usage, and strategic dispatching of DG resources. …”
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Chance-constrained optimal schedule of battery energy storage considering the uncertainties of renewable generation by Zhi Li, Dawei Xie, Haifeng Ye, Yujun Li, Jinzhong Li, Yichi Chen, Yue Yang

“…Since renewable energy generation has strong uncertainties and pure conventional unit dispatch schemes are limited by the unit-operating capacities, such scheduling is inapplicable for power systems with high proportions of renewable energy sources (RESs). We propose an optimal scheduling model for battery energy storage systems (BESSs) by considering the uncertainties of RESs. …”
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An eigenvalue-driven framework for the ranking and selection of optimal geological CO2 storage sites by Soha Iranfar, Farshad Sadeghpour, Abbas Khaksar Manshad, Meysam Naderi, Mahmood Shakiba

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Ensemble Learning-Based Wine Quality Prediction Using Optimized Feature Selection and XGBoost by Sonam Tyagi, Ishwari Singh Rajput, Bhawnesh Kumar, Harendra Singh Negi

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Machine learning optimization of microwave-assisted extraction of phenolics and tannins from pomegranate peel by Fatemeh Mobasheri, Mostafa Khajeh, Mansour Ghaffari-Moghaddam, Jamshid Piri, Mousa Bohlooli

“…However, there is still a lot of difficulty dealing with the extraction of these substances due to the limitations of traditional methods. Microwave-assisted extraction (MAE) has shown promise, but optimizing it for maximum efficiency and yield remains a challenge. …”
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Federated Learning for Fall Detection With Multimodal Residual Fusion and Pareto-Optimized Client Selection by Bao-Quan Wang, Fan Yang, Yi Wang, Fan Zhao, Yun-Fei Han, Yu-Peng Ma

“…Secondly, to address variations in data modalities, distributions, and quality across clients, by considering all client factors rather than treating clients as independent, five innovative evaluation metrics are designed to assess the convergence and generalization performance of the local models. Finally, a Pareto-optimized client selection method is introduced to efficiently select reliable clients for global aggregation, ensuring both the stability and robustness of the global model. …”
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Induced zero-phase synchronization as a potential neural code for optimized visuomotor integration by Kirstin-Friederike Heise, Geneviève Albouy, Nina Dolfen, Ronald Peeters, Dante Mantini, Stephan P. Swinnen

“…This optimized information integration (‘predictive coding') results in a global behavioral advantage of anticipated action in the presence of uncertainty. …”
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Desalination and Sterilization of Sea Water Using Active Optimized Optical Heat Concentration System by Hifsa Shahid, Umar Rashid, Haris Anwaar, Muhammad Salim Butt, Fahim Gohar Awan, Farhan Ahmad Butt, Frederic Nzanywayingoma

“…Carbon emissions from these sources cause breathing problems and ozone depletion. …”
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Comparing the impact of cumulative insulin resistance surrogates exposure on stroke: optimizing prevention strategies by Dezhi Hong, Xiaohui Li, Guotai Sheng, Hongyi Yang, Wei Wang, Yang Zou

“…Methods The study population was sourced from the China Health and Retirement Longitudinal Study (CHARLS2011-2018). …”
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Integrating optimal terrain representations from public DEMs using spaceborne LiDAR by Xingang Zhang, Shanchuan Guo, Haowei Mu, Bo Yuan, Zilong Xia, Xiaoquan Pan, Hong Fang, Pengfei Tang, Peijun Du

“…Due to differences in data sources and processing methods, the accuracy of Digital Elevation Models (DEMs) varies greatly in different regions, which poses challenges for users in data selection. …”
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Development and Optimization of a Novel Deep Learning Model for Diagnosis of Quince Leaf Diseases by A. Naderi Beni, H. Bagherpour, J. Amiri Parian

“…Today, deep convolutional neural networks (DCNNs), a novel approach to image classification, have become the most crucial detection method. DCNNs improve detection or classification accuracy by developing machine-learning models with many hidden layers to extract optimal features. …”
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Construction of and Low-Carbon Optimization Strategies for Block-Level Carbon Emission Control Unit by Wenjie LI, Yingsheng ZHENG, Qingfang ZHANG, Qiuyun ZENG

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Siting and Sizing Method of GFM Converters Based on Genetic Algorithm by Wentao Sun, Yi Ge, Guojing Liu, Hui Cai, Quanquan Wang, Xingning Han, Wanchun Qi

“…The rising integration of renewable energy sources has resulted in a diminished capacity for voltage support within the system, which is characterized by low inertia and a reduced short circuit ratio (SCR). …”
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Dam Risk Identification Method Based on EAHP-EWM-TOPSIS by ZHAN Mingqiang, CHEN Bo, TIAN Jufei, HU Zelin, PAN Xinmei

“…To address the uncertainty and fuzziness of dam risk indicators, this paper proposes a dam risk identification model based on the EAHP-EWM-TOPSIS game combination model.This is to determine the subjective and objective weights of risk evaluation indicators by extension analytic hierarchy process and entropy weight method respectively.Meanwhile, the game theory is introduced to determine the optimal combination weight, and a weighted standardized decision matrix is constructed.Then, the TOPSIS method is employed to calculate the relative proximity between each risk factor and the ideal solution as the basis for ranking risk factors to identify the main risk sources of dams.The engineering example shows that the main risk factors affecting the safe service of the dams are dam seepage and cross-bank seepage, with the relative proximity of 0.712 7 and 0.855 2 respectively.The identified main risk factors are consistent with the actual engineering situation, which verifies the model effectiveness.The differentiation degree of this model is more obvious than other models, providing an effective basis for dam reinforcement and risk management.…”
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