Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problems using evolutionary computation. This paper proposes treating these problems as multi-objective multi-criteria optimization problems and develops an algorithm framework that utilizes the knowledge of all tasks in the same population. The algorithm selects fitness evaluation functions as criteria, guided by a probability-based selection strategy and an adaptive parameter learning method. Extensive experiments show the effectiveness and efficiency of the proposed algorithm. Treating MO-MTOP as MO-MCOP is a potential and promising direction for solving these problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Li Yan, Zhipeng Zhang, Jing Liang, Boyang Qu, Kunjie Yu, Kongyuan Wang
Summary: This paper proposes an adaptive segmented multi-objective evolutionary network architecture search (ASMEvoNAS) method, which efficiently searches for network architectures through adaptive segmented evaluation strategy, preference-based pre-selection strategy, and novel gene reservation-based crossover and directed connection-based mutation. Experimental results demonstrate that ASMEvoNAS achieves promising performance on CIFAR-10, CIFAR-100, and ImageNet datasets.
APPLIED SOFT COMPUTING
(2023)
Article
Thermodynamics
Ya Ge, Yousheng Lin, Qing He, Wenhao Wang, Jiechao Chen, Si-Min Huang
Summary: This paper investigated the geometric optimization of segmented thermoelectric generator (STEG) modules to improve energy harvesting performance, optimizing length ratios of two TE materials for maximum output power. The results showed that optimal STEGs outperform non-segmented TEGs, and proposed hybrid TEG modules to reduce manufacturing difficulty and costs.
Article
Automation & Control Systems
Huangke Chen, Ran Cheng, Witold Pedrycz, Yaochu Jin
Summary: This paper proposes a method to solve multiobjective optimization problems through multi-stage evolutionary search, highlighting convergence and diversity in different search stages. The algorithm balances and addresses the issues in multiobjective optimization through two stages.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Wei Fang, Qiang Zhang, Jun Sun, Xiaojun Wu
Summary: This paper proposes a multi-objective problem model and an improved evolutionary algorithm for high quality pattern mining. Experimental results demonstrate that the proposed method outperforms existing algorithms in terms of efficiency, quality, and convergence speed.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Multidisciplinary Sciences
Jinmeng Wu, Yan Chen, Yinke Dou, Chunyan Ma, Qian Du, Qiang Liu
Summary: This study focuses on optimizing the structure of the hot-end heat collection pipe to increase TEG output power. By using a finite element simulation model and a multi-objective genetic algorithm for optimization, the best size is determined, and the accuracy of the model is verified through experimentation.
ADVANCED THEORY AND SIMULATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Zhenyu Lei, Shangce Gao, Zhiming Zhang, MengChu Zhou, Jiujun Cheng
Summary: Protein structure prediction is a significant biocomputing challenge, and a complete solution using computational methods has not yet been achieved. This study proposes a many-objective approach to improve the accuracy of predicting protein structures by utilizing evolutionary algorithms. Experimental results show that this method can yield more accurate and efficient protein structure predictions.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Elaine Guerrero-Pena, Aluizio F. R. Araujo
Summary: Dynamic multi-objective evolutionary algorithms can address multi-objective optimization problems by predicting and responding to changes, with prediction-based methods showing promise. Through the use of objective space prediction strategy and change reaction mechanism, the proposed DOSP-NSDE demonstrates competitiveness in experiments.
APPLIED SOFT COMPUTING
(2021)
Article
Thermodynamics
Yajing Sun, Pengcheng Zhai, Shuhao Wang, Bo Duan, Guodong Li, Gang Chen
Summary: This study presents a novel approach combining multi-objective genetic algorithm with finite element method for the multi-parameter and multi-objective optimization of segmented annular thermoelectric generator. The optimization results highlight the importance of emphasizing diversity between p- and n- type TE legs as well as recognizing the different optimal geometrical structures for two types of TE legs, which have been always overlooked in previous researches.
THERMAL SCIENCE AND ENGINEERING PROGRESS
(2024)
Article
Computer Science, Theory & Methods
Ye Tian, Langchun Si, Xingyi Zhang, Ran Cheng, Cheng He, Kay Chen Tan, Yaochu Jin
Summary: This article provides a comprehensive survey of state-of-the-art MOEAs for solving large-scale multi-objective optimization problems, categorizing them into different types and discussing their strengths and weaknesses. It also reviews benchmark problems for performance assessment and important applications, while also addressing remaining challenges and future research directions in evolutionary large-scale multi-objective optimization.
ACM COMPUTING SURVEYS
(2021)
Article
Computer Science, Artificial Intelligence
Wei Kun Li, Hao Chen, Wei Cheng Cui, Chang Hui Song, Lin Ke Chen
Summary: Fish-inspired motion is an important research area with many applications, such as underwater vehicles or robotic fish control design. This paper proposes a multi-objective evolutionary design of a central pattern generator network to control biomimetic robotic fish, and adopts fuzzy theory for solution selection. Simulations and experiments demonstrate the effectiveness and good performance of the proposed control model.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Theory & Methods
Chu-ge Wu, Wei Li, Ling Wang, Albert Y. Zomaya
Summary: The paper proposes a fuzzy logical offloading strategy for IoT applications to optimize both agreement index and robustness. A multi-objective Estimation of Distribution Algorithm (EDA) is designed to learn and optimize the fuzzy offloading strategy from a diversity of the applications, by partitioning applications into independent clusters to save system resources.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Energy & Fuels
Chika Maduabuchi, Mohammad Alobaid
Summary: This study aims to enhance the performance of a concentrating solar two-stage TEG by conducting a comprehensive geometry and stage number optimization. Results show that simultaneously increasing the heights of n- and p-high temperature materials is the most effective optimization method, achieving a maximum efficiency.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Automation & Control Systems
Zheng-Yi Chai, Xu Liu, Ya-Lun Li
Summary: This paper proposes an improved dynamic multi-objective evolutionary optimization algorithm (DMOEA/D-COPMEC) for the computation offloading problem in mobile edge computing (MEC). The algorithm detects environmental changes using a fixed detector and re-locates population individuals based on historical change predictions to balance application delay and terminal energy consumption.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Yong-Feng Ge, Zhi-Hui Zhan, Jinli Cao, Hua Wang, Yanchun Zhang, Kuei-Kuei Lai, Jun Zhang
Summary: This paper proposes a distributed segment-based genetic algorithm (DSGA) for addressing the data partition problem in outsourced distributed databases. The algorithm protects data privacy while achieving a trade-off between communication cost and load balance. By introducing a digit-based anonymity strategy and segment-based operators, the performance and search efficiency of the algorithm are improved.
INFORMATION SCIENCES
(2022)
Article
Thermodynamics
Ding Luo, Ye Zhao, Yuying Yan, Hao Chen, Wei-Hsin Chen, Ruochen Wang, Ying Li, Xuelin Yang
Summary: In this study, two transient models, a transient fluid-thermal-electric multiphysics numerical model and a hybrid transient CFD-analytical model, are proposed to predict the dynamic performance of automobile thermoelectric generator systems. The models consider the heat source fluctuation, temperature dependence of thermoelectric materials, and the coupling of different physical fields. The results show that the dynamic output power is mainly related to the exhaust temperature due to thermal inertia, while the dynamic conversion efficiency is mainly related to the exhaust mass flow rate. The hybrid model overestimates the output performance, particularly the conversion efficiency, with average errors of 2.90% and 13.58% for output power and conversion efficiency, respectively, compared to the numerical model. The transient models predict lower output performance compared to steady-state analysis, and the models are experimentally verified. This work fills a gap in theoretical models for predicting the dynamic response characteristics of automobile thermoelectric generator systems.
APPLIED THERMAL ENGINEERING
(2023)
Article
Agricultural Engineering
Congyu Zhang, Wei-Hsin Chen, Shih-Hsin Ho
Summary: This study compares the economic feasibility of different torrefaction methods and evaluates the environmental pollution potential of conventional torrefaction and microwave torrefaction using spent coffee grounds as feedstocks. The results indicate that microwave torrefaction is a more economically efficient approach for biomass upgrading. Additionally, the environmental impact assessment shows that microwave torrefaction has lower environmental impact compared to conventional torrefaction, especially under light conditions. However, the environmental impact of microwave torrefaction increases at a higher rate, including resource depletion and terrestrial ecotoxicity, compared to conventional torrefaction.
BIOMASS & BIOENERGY
(2023)
Article
Chemistry, Multidisciplinary
Hoang Anh Tuan, Ashok Pandey, Chen Wei-Hsin, Shams Forruque Ahmed, Sandro Nizetic, Kim Hoong Ng, Zafar Said, Duong Xuan Quang, Umit Agbulut, Hady Hadiyanto, Nguyen Xuan Phuong
Summary: Hydrogen energy is considered an attractive alternative to fossil fuels due to its environmental friendliness. Photocatalysis-derived hydrogen from water splitting is believed to be the optimal solution for meeting long-term sustainability and increased energy demands. Metal and carbon-supported photocatalysts show great potential for solar-driven hydrogen production from water. This review discusses the important aspects of different photocatalytic genres and provides new directions for more advanced performance.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2023)
Article
Agricultural Engineering
Thanh-Binh Nguyen, Thi-Kim-Tuyen Nguyen, Wei-Hsin Chen, Chiu-Wen Chen, Xuan-Thanh Bui, Anil Kumar Patel, Cheng-Di Dong
Summary: In this study, the effectiveness of sunflower seed husk biochar (HZSF) prepared by ZnCl2-activated and hydrothermal carbonization in removing tetracycline (TC) from an aqueous solution was studied. The physical and chemical properties of the materials were characterized using different surface analysis methods. The specific surface area of HZSF was significantly enhanced compared to non-modified biochar, resulting in improved TC adsorption capacity.
BIORESOURCE TECHNOLOGY
(2023)
Article
Agricultural Engineering
Shih-Wei Yen, Dillirani Nagarajan, Wei-Hsin Chen, Duu-Jong Lee, Jo-Shu Chang
Summary: In this study, Aurantiochytrium sp. CJ6 was cultivated with heterotrophic fermentation using sorghum distillery residue (SDR) hydrolysate as the feedstock, without the addition of nitrogen sources. Optimal operating parameters were determined, and batch cultivation achieved high biomass concentration and astaxanthin content. Continuous-feeding fed-batch (CF-FB) fermentation further increased biomass concentration and astaxanthin production. This study demonstrates the potential of using SDR as a sustainable feedstock for the production of high-value products like astaxanthin.
BIORESOURCE TECHNOLOGY
(2023)
Article
Agricultural Engineering
Thi-Kim-Tuyen Nguyen, Thanh-Binh Nguyen, Wei-Hsin Chen, Chiu-Wen Chen, Anil Kumar Patel, Xuan-Thanh Bui, Linjer Chen, Reeta Rani Singhania, Cheng-Di Dong
Summary: In recent years, there has been an increase in the unnecessary overuse of antibiotics globally, resulting in the contamination of water with antibiotics. This study examines the adsorption behavior of four antibiotics onto H3PO4-activated sunflower seed husk biochar (PSF). The results show that H3PO4 enhances the specific surface area and creates a mesoporous structure of the biochar. The adsorption mechanism of antibiotics on PSF is governed by complex mechanisms, including chemisorption.
BIORESOURCE TECHNOLOGY
(2023)
Article
Green & Sustainable Science & Technology
Zafar Said, Maham Aslam Sohail, Adarsh Kumar Pandey, Prabhakar Sharma, Adeel Waqas, Wei-Hsin Chen, Phuoc Quy Phong Nguyen, Van Nhanh Nguyen, Nguyen Dang Khoa Pham, Xuan Phuong Nguyen
Summary: Integrating nanotechnology into phase change materials (PCMs) is a novel approach to improve thermal properties and performance in thermal energy storage. Nanofluids and nano-enhanced PCMs (NEPCMs) show promise in increasing thermal conductivity and improving energy charging and discharging duration. This review discusses recent advances in applying nanotechnology to PCMs, focusing on its applications in the solar energy sector. The review also highlights the potential benefits, challenges, and environmental concerns associated with using nanofluids and NEPCMs in various applications.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Thermodynamics
Wei-Hsin Chen, Manuel Carrera Uribe, Ding Luo, Liwen Jin, Lip Huat Saw, Ravita Lamba
Summary: In recent years, the use of unsustainable and inefficient energy sources has led to environmental pollution. Thermoelectric generators (TEGs) have the potential to recover wasted heat, but their energy conversion efficiency needs improvement. This study optimizes commercially available TEGs by considering hot side temperature, heat sink size, and wind airspeed. The results show that hot side temperature has the greatest influence on the output power, while the impact of wind speed is minimal.
APPLIED THERMAL ENGINEERING
(2023)
Review
Agricultural Engineering
Amit Kumar Sharma, Praveen Kumar Ghodke, Nishu Goyal, Prakash Bobde, Eilhann E. Kwon, Kun -Yi Andrew Lin, Wei-Hsin Chen
Summary: This review investigates the possibilities and recent technological advancements for synthesizing biochar from pine waste, as well as explores techniques for enhancing its properties and integrated applications in various fields. The paper also highlights the limitations of current strategies and emphasizes the need for further research to address challenges in pine waste-based biochar synthesis. By promoting sustainable and effective utilization of pine wastes, this review contributes to environmental conservation and resource management.
BIORESOURCE TECHNOLOGY
(2023)
Article
Agricultural Engineering
Congyu Zhang, Wei-Hsin Chen, Shih-Hsin Ho, Ying Zhang, Steven Lim
Summary: This study conducts a comparative advantage analysis of oxidative torrefaction of corn stalks to explore the benefits of oxidative torrefaction for upgrading biochar fuel properties. The results show that oxidative torrefaction is more efficient in achieving mass loss, energy density improvement, elemental carbon accumulation, and surface functional groups removal, leading to better fuel properties. The study also establishes a linear correlation between comprehensive pyrolysis index, torrefaction severity index, and elemental carbon and oxygen component variation.
BIORESOURCE TECHNOLOGY
(2023)
Article
Agricultural Engineering
Charles B. Felix, Wei-Hsin Chen, Jo-Shu Chang, Young -Kwon Park, Samrand Saeidi, Gopalakrishnan Kumar
Summary: Oxidative torrefaction is recommended for improving the fuel properties of microalgae as solid biofuels. Temperature, time, and O2 concentration have significant effects on various parameters such as solid yield, energy yield, and higher heating value. The optimal conditions for this process are 200°C, 10.6 min, and 12% O2, resulting in an energy yield of 98.73% and an enhancement factor of 1.08. Furthermore, it shows higher reactivity under an air environment compared to inert torrefaction conditions.
BIORESOURCE TECHNOLOGY
(2023)
Article
Biotechnology & Applied Microbiology
Larissa Richa, Baptiste Colin, Anelie Petrissansa, Ciera Wallace, Allen Hulette, Rafael L. Quirino, Wei-Hsin Chen, Mathieu Petrissans
Summary: Torrefaction is a potential pretreatment method to produce biochar for fuel or construction material. The role of potassium in the torrefaction process and its effect on thermal degradation were investigated. It was found that potassium acted as a catalyst, promoting char formation and increasing char content in the biomass.
ENVIRONMENTAL TECHNOLOGY & INNOVATION
(2023)
Article
Biotechnology & Applied Microbiology
C. Naveen, Praveen Kumar Ghodke, Amit Kumar Sharma, Wei-Hsin Chen
Summary: This study investigates the feasibility of producing energy from PMDE solid waste through pyrolysis. The pyrolysis process results in the formation of condensable volatiles, non-condensable gases, and solid biochar. The non-condensable gas contains CO, H2, and CH4 in significant amounts, as well as other gases in minor quantities. Kinetic and thermodynamic analysis were performed to understand the reaction mechanism, and a bio-circular economic approach for PMDE solid waste was presented.
ENVIRONMENTAL TECHNOLOGY & INNOVATION
(2023)
Article
Thermodynamics
Congyu Zhang, Yong Zhan, Wei-Hsin Chen, Shih-Hsin Ho, Young-Kwon Park, Alvin B. Culaba, Ying Zhang
Summary: This study evaluates the relationship between torrefaction parameters and fuel properties using various indicators. It finds that some indicators can accurately reflect the changes in different fuel properties. Additionally, the variations of torrefaction severity index and contact angle are correlated to the changes in the fuel property of the torrefied biochar.
Article
Environmental Sciences
Larissa Richa, Baptiste Colin, Anelie Petrissans, Jasmine Wolfgram, Ciera Wallace, Rafael L. Quirino, Wei-Hsin Chen, Mathieu Petrissans
Summary: This study evaluates the possibility of using catalytic torrefaction as a pretreatment to improve wood pyrolysis and combustion for greener biochar production. The findings show that catalytic torrefaction can significantly decrease the devolatilization peak during combustion, making the wood's combustion similar to that of coal.
ENVIRONMENTAL POLLUTION
(2024)
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.
