Article
Green & Sustainable Science & Technology
Ivan Garcia Kerdan, David Morillon Galvez
Summary: This paper introduces an ANNEXE building design optimization framework that combines artificial neural network and exergy analysis to improve building energy efficiency. The framework achieves significant improvements in computational times and provides a robust optimization tool for designers and decision makers.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Green & Sustainable Science & Technology
Salih Himmetoglu, Yilmaz Delice, Emel Kizilkaya Aydogan, Burak Uzal
Summary: A new multi-objective approach is proposed in this study to determine the most suitable green envelope designs for buildings in different climate and earthquake zones, considering CO2 emissions, heating/cooling energy consumption, and material cost. The approach combines EnergyPlus building performance simulation program, artificial neural network, and genetic algorithm to efficiently search through the envelope alternative space.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2022)
Article
Energy & Fuels
Yuxiao Zhu, Daniel W. Newbrook, Peng Dai, C. H. Kees de Groot, Ruomeng Huang
Summary: This study demonstrates the application of artificial neural network, a deep learning technique, in forward modeling the maximum power generation and efficiency of a thermoelectric generator for the first time. The neural networks, with the coupling of genetic algorithm, can optimize the geometrical structure of the generator quickly and accurately, providing a new and cost-effective approach for system level design and optimization of thermoelectric generators and other energy harvesting technologies.
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Siti Aishah Azhar, Siti Syatirah Muhammad Sidik, Mohd Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Pateema Azeyan Pakruddin, Nurul Atirah Pauzi, Siti Nurhidayah Mat Nawi
Summary: This study addresses the overfitting problem of the existing Discrete Hopfield Neural Network in handling real-life optimization problems by introducing a non-systematic Satisfiability model that promotes diversification of solutions. Experimental results show that the Genetic Algorithm outperforms other algorithms in optimizing the logic and training phases.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Patrick G. Mongan, Vedant Modi, John W. McLaughlin, Eoin P. Hinchy, Ronan M. O'Higgins, Noel P. O'Dowd, Conor T. McCarthy
Summary: This study investigates the ultrasonic welding technique for composite materials, aiming to optimize the welding process parameters and enhance the joint strength and visual quality through a parametric study and a machine learning model.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Article
Energy & Fuels
K. Venkateshwar, S. H. Tasnim, S. A. Gadsden, S. Mahmud
Summary: This study aims to enhance the performance of Thermal Energy Storage (TES) system by optimizing the porosity distribution of graded metal foam. A numerical model is used to quantify the influence of graded metal foam on heat transfer rate, and an Artificial Neural Network (ANN) model is trained to optimize the porosity distribution using a Genetic algorithm. The results demonstrated significant heat transfer enhancement with the optimal porosity distribution.
JOURNAL OF ENERGY STORAGE
(2022)
Article
Construction & Building Technology
X. J. Luo, Lukumon O. Oyedele
Summary: This study develops a novel life cycle optimisation strategy to consider the impacts of climate change on building energy performance and evaluates the performance of retrofitting measures. The research finds that there is a significant discrepancy between optimal retrofitting solutions determined using current and future weather conditions, leading to over- or under-estimation of cost, energy, and carbon emissions.
ENERGY AND BUILDINGS
(2022)
Article
Energy & Fuels
Yule Wang, Arwa Abdulkreem AL-Huqail, Shadi Salimimoghadam, Khidhair Jasim Mohammed, Amin Jan, H. Elhosiny Ali, Mohamed Amine Khadimallah, Hamid Assilzadeh
Summary: Eggshell concrete is a sustainable green material that utilizes eggshell powder to improve energy stability and strength. The optimal replacement percentage of eggshell powder is found to be 5% to 10% for maximum strength. The hybrid model of artificial neural network and genetic algorithm proves to be an effective tool for predicting the mechanical properties of eggshell concrete.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Computer Science, Information Systems
Yanhua Lu, Xuehui Gong, Andrew Byron Kipnis
Summary: This study combines neural networks and grey correlation method to analyze the energy consumption of individual buildings in universities, establishing a reliable model for qualitative analysis.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Multidisciplinary Sciences
Ta-Feng Lou, Wei-Hsi Hung
Summary: The purpose of our bibliometric research was to analyze the trends of neural networks and genetic algorithms, two well-known classical AI algorithms. We referred to the innovative research articles published in Symmetry to explore new applications for these algorithms. By searching the SSCI database, we obtained 951 publications on neural networks and 878 publications on genetic algorithms, which were categorized into eight groups for deep analysis. The results showed that the use of neural networks is growing more rapidly than genetic algorithms, mainly due to the boom in deep learning development.
Article
Computer Science, Artificial Intelligence
Mehmet Ozcalici, Mete Bumin
Summary: In this study, filter rule parameters were optimized using genetic algorithms and stock selection was performed with artificial neural networks to achieve excess returns over the market average. The results suggest that Borsa Istanbul may be a weak form efficient market, but utilizing artificial neural networks can lead to higher profits for investors.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Mehmet Ozcalici, Mete Bumin
Summary: The study explores the use of filter rule and genetic algorithm for identifying profitable trading points in stock markets, and utilizes artificial neural networks for stock selection. The results demonstrate significantly higher returns for the selected stocks compared to the buy and hold strategy.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Thermodynamics
Amir Ebrahimi-Moghadam, Mahmood Farzaneh-Gord, Ali Jabari Moghadam, Nidal H. Abu-Hamdeh, Mohammad Ali Lasemi, Ahmad Arabkoohsar, Ashkan Alimoradi
Summary: A novel trigeneration district energy system is designed and evaluated, considering energy, exergy, exergoeconomic, and exergoenvironmental aspects. The system recovers wasted heat to generate power, heat, and cold, and optimization procedures determine the system's optimal parameters. Sensitivity analysis shows that compressor pressure ratio and heat exchanger pinch-point temperature difference have the highest impact on the system.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Energy & Fuels
Ling Zheng, Bin Zhou, Yijia Cao, Siu Wing Or, Yong Li, Ka Wing Chan
Summary: This paper proposes a distributed multi-energy demand response methodology for optimal coordinated operation of smart building clusters. The proposed method utilizes a hierarchical framework and information exchange between aggregators and buildings to achieve optimal multi-energy coordination. By dynamically correcting and optimizing the approach, the impact of prediction uncertainties is reduced. Experimental results demonstrate the superiority of the proposed methodology in solving the optimal synergistic operation problem of smart buildings.
Article
Chemistry, Multidisciplinary
Marco Pittarello, Massimiliano Scarpa, Aurora Greta Ruggeri, Laura Gabrielli, Luigi Schibuola
Summary: The developed artificial neural network tools can quickly predict building energy consumption without the support of BEM. These tools can automatically estimate energy consumption based on limited property data, facilitating comparative design options and optimization analyses for a large number of buildings.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Shaun Howell, Thomas Beach, Yacine Rezgui
Summary: This paper proposes a methodology to address the sustainability and intelligence challenges of urban environments, successfully applied in the smart water domain to achieve positive results in requirement elicitation, testing, and deployment.
REQUIREMENTS ENGINEERING
(2021)
Article
Green & Sustainable Science & Technology
Andrei Hodorog, Ioan Petri, Yacine Rezgui, Jean-Laurent Hippolyte
Summary: The adoption of BIM technology and the push for decarbonisation in the built environment have led to a need for redefining construction personnel positions and skills. The research utilizes text mining and ontological dependency analysis to explore the correlation between BIM roles and skills, providing guidance for training and education programs.
CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY
(2021)
Correction
Biology
Xindong Chen, Hanxing Zhu, XiQiao Feng, Xiaona Li, Yongtao Lu, Zuobin Wang, Yacine Rezgui
COMMUNICATIONS BIOLOGY
(2021)
Article
Energy & Fuels
Ioan Petri, Omer Rana, Yacine Rezgui, Fodil Fadli
Summary: Integrating data analytics, optimisation and dynamic control to support energy services has attracted significant interest in recent years. The combination of IoT and edge computing allows connectivity between energy systems and infrastructure, enabling the implementation of energy efficiency/optimisation and cost reduction strategies.
Review
Energy & Fuels
Ateyah Alzahrani, Ioan Petri, Yacine Rezgui, Ali Ghoroghi
Summary: Marine activities in seaports contribute to about 3% of global carbon emissions, prompting the need for decarbonization and smarter, greener energy systems. This paper reviews existing research and discusses future research directions for the energy management of seaports, emphasizing the importance of adapting energy regulatory landscape to meet EU phased energy reduction targets.
ENERGY STRATEGY REVIEWS
(2021)
Review
Green & Sustainable Science & Technology
Mohammad Hosein Abbasi, Badr Abdullah, Muhammad Waseem Ahmad, Ali Rostami, Jeff Cullen
Summary: Decarbonisation of heating and cooling in the built environment is recognized as essential for achieving energy and climate change targets, with heat pumps being a validated technology in Europe. However, challenges and uncertainties remain in research, practices, and policies related to heat pumps. Real-world case studies provide practical evidence that can help accelerate the adoption of heat pumps, highlighting the significant potential of hybrid heat pumps and other consolidated solutions. Regulatory provisions and building upgrades are crucial for keeping pace with the heat pump initiatives across Europe.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Engineering, Environmental
Abdulrahman Fnais, Yacine Rezgui, Ioan Petri, Thomas Beach, Jonathan Yeung, Ali Ghoroghi, Sylvain Kubicki
Summary: This paper reviews the current research in life cycle assessment (LCA) applied to buildings, focusing on trends and identifying gaps for future research. A systematic literature review was conducted to identify current research and applications of LCA in buildings. The paper argues for the development of new generation LCA methods that can continuously learn from real-time data and inform effective operation and management strategies for buildings. It also emphasizes the importance of considering the time dimension in product system modeling and the combination of life cycle impact assessment models for more comprehensive and reliable LCA results. The paper promotes the concept of semantic-based dynamic LCA to achieve cradle-to-grave-to-reincarnation environmental sustainability capability and highlights the need to leverage digital building resources for accurate and reliable environmental assessments.
INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT
(2022)
Review
Engineering, Environmental
Ali Ghoroghi, Yacine Rezgui, Ioan Petri, Thomas Beach
Summary: This study explores the application of machine learning methods in life cycle assessment (LCA) and suggests that ML can be a useful tool in certain aspects of LCA, particularly in optimization scenarios. By integrating ML methods into existing inventory databases, the LCA process can be streamlined.
INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT
(2022)
Article
Energy & Fuels
Ateyah Alzahrani, Ioan Petri, Ali Ghoroughi, Yacine Rezgui
Summary: This article discusses the increasing pressure on seaports to reduce carbon footprint and increase energy efficiency and global competitiveness. The fishing industry, which is one of the most energy-intensive activities at seaports, has seen a dramatic increase in global human consumption of fish. The paper presents a roadmap to convert fishing ports into carbon-free ports, aiming to reduce energy dependence from the national grid and promote sustainable energy practices.
Article
Energy & Fuels
Mariam Elnour, Yassine Himeur, Fodil Fadli, Hamdi Mohammedsherif, Nader Meskin, Ahmad M. Ahmad, Ioan Petri, Yacine Rezgui, Andrei Hodorog
Summary: This study proposes a neural network-based model predictive control system for managing and optimizing the HVAC system of a sports hall. The neural network model outperforms other machine learning models and achieves significant energy reduction while improving indoor environmental quality.
Article
Green & Sustainable Science & Technology
Mariam Elnour, Fodil Fadli, Yassine Himeur, Ioan Petri, Yacine Rezgui, Nader Meskin, Ahmad M. Ahmad
Summary: This research presents the first review article addressing the research gap in building operation management and optimization for sports facilities. The study highlights the importance of considering climate zones and facility characteristics. The findings reveal limited research on sports facility management and optimization compared to residential and commercial buildings, with a majority of studies focused on facilities in cold regions. The study suggests future research directions, including the use of deep learning, modular solutions, and renewable energy systems.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Construction & Building Technology
Giulia Cere, Yacine Rezgui, Wanqing Zhao, Ioan Petri
Summary: This research presents a risk-based approach to assess the risk of buildings under seismic conditions by optimizing the layout and thickness of specified shear walls, validated on a building in Beichuan, China. The methodology, validated on a building in Beichuan, China, demonstrates a reduction in risk and additional benefits.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Construction & Building Technology
Andrei Hodorog, Ioan Petri, Yacine Rezgui
Summary: This article highlights the importance of social media data analysis in decision making within the context of a smart city. It presents the use of Natural Language Processing (NLP) techniques for real-time automated event detection on Twitter. Through semantic-based taxonomy and multiple regression analysis, it reveals the relationships between events and citizen satisfaction, providing valuable data for informed decision making.
SUSTAINABLE CITIES AND SOCIETY
(2022)
Article
Engineering, Civil
Kaznah Alshammari, Thomas Beach, Yacine Rezgui
Summary: Recent technological developments in the construction industry aim to create smart cities by using Cyber-Physical Systems to enhance information models like BIM. While digital twins are expected to provide new possibilities for IoT systems through monitoring and simulation, security is rarely fully considered in this rapidly evolving field.
JOURNAL OF INFORMATION TECHNOLOGY IN CONSTRUCTION
(2021)
Article
Construction & Building Technology
Raed Alelwani, Muhammad Waseem Ahmad, Yacine Rezgui
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.
