Article
Energy & Fuels
Raad Z. Homod, Hayder Ibrahim Mohammed, Aissa Abderrahmane, Omer A. Alawi, Osamah Ibrahim Khalaf, Jasim M. Mahdi, Kamel Guedri, Nabeel S. Dhaidan, A. S. Albahri, Abdellatif M. Sadeq, Zaher Mundher Yaseen
Summary: This study proposes a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adds a pre-cooling coil in the air handling unit (AHU) to alleviate the coupling issue in HVAC systems. The DCLTML algorithm shows promising results in controlling HVAC systems, with significant energy savings and improved environmental comfort.
Article
Thermodynamics
Xiangfei Liu, Mifeng Ren, Zhile Yang, Gaowei Yan, Yuanjun Guo, Lan Cheng, Chengke Wu
Summary: This paper proposes a novel HVAC control system based on a multi-step predictive deep reinforcement learning algorithm. The system predicts the outdoor ambient temperature using the GC-LSTM algorithm and combines it with the DDPG algorithm to adjust the output power of the HVAC system based on the dynamic changing of electricity prices. Simulation results demonstrate the effectiveness of the system in saving costs while maintaining user comfort.
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.
Article
Construction & Building Technology
Kari Alanne, Seppo Sierla
Summary: The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change, while the rapid evolution of artificial intelligence and machine learning has equipped buildings with an ability to learn. Research has mainly focused on specific machine learning applications for different phases of a building's life-cycle, lacking a holistic perspective on the integration of smart technologies at the system level. Enhancing buildings' adaptability to unpredicted changes through AI-initiated learning processes and using digital twins as training environments can greatly improve energy efficiency at the level of HVAC control and electricity market participation.
SUSTAINABLE CITIES AND SOCIETY
(2022)
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
Construction & Building Technology
Anchal Gupta, Youakim Badr, Ashkan Negahban, Robin G. Qiu
Summary: This research introduces a Deep Reinforcement Learning-based heating controller to enhance thermal comfort and reduce energy costs in smart buildings. Through simulation experiments, it is demonstrated that the DRL-based controller outperforms traditional thermostat controllers, showing improvements in thermal comfort and energy savings. Additionally, when dealing with multiple buildings, decentralized control proves to be more effective than centralized control, especially in areas with varying building characteristics and setpoint temperatures.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Multidisciplinary Sciences
Antonio Martinez Ibarra, Aurora Gonzalez-Vidal, Antonio Skarmeta
Summary: The current cost of energy in the field of climate control is crucial and needs to be reduced. The expansion of ICT and IoT allows for the deployment of sensors and computation infrastructure, presenting an opportunity to optimize energy management. Data on building conditions is essential for developing efficient control strategies. Here, we present a dataset from the Pleiades building, collected for almost a year, which can be used for modeling temperature and consumption using AI algorithms.
Article
Thermodynamics
Haosen Qin, Zhen Yu, Tailu Li, Xueliang Liu, Li Li
Summary: This paper proposes a model-free optimal control method based on deep reinforcement learning for controlling the heat pump start/stop and room temperature setting in residential buildings, aiming to improve energy efficiency of demand-side. The simulation results show that the method can achieve the highest comprehensive reward by coordinating monitoring data, weather forecasts, and building thermal inertia.
Article
Energy & Fuels
Bin Zhang, Weihao Hu, Amer M. Y. M. Ghias, Xiao Xu, Zhe Chen
Summary: This paper proposes a model-free multi-agent deep reinforcement learning algorithm for intelligent management and electricity bill saving in multiple multi-zone buildings while alleviating voltage regulation stress on the grid. The algorithm achieves building-side and grid-level objectives through centralized training and decentralized execution, with an attention mechanism to enhance training and preserve privacy.
Article
Computer Science, Information Systems
Yi Peng, Haojun Shen, Xiaochang Tang, Sizhe Zhang, Jinxiao Zhao, Yuru Liu, Yuming Nie
Summary: Finding the optimal energy-saving control strategy for HVAC systems has become crucial in realizing energy savings, emission reductions, and green buildings. Deep reinforcement learning (DRL) provides new ideas for HVAC energy consumption optimization. This study proposes a DRL-based framework for HVAC energy consumption optimization, which includes a CNN-LSTM model for energy consumption prediction and an enhanced DDPG algorithm for real-time energy consumption control.
Article
Thermodynamics
Christian Blad, Simon Bogh, Carsten Skovmose Kallesoe
Summary: This paper presents a novel framework for Offline Reinforcement Learning with online fine-tuning for HVAC systems. The framework enables pre-training in a black box model environment and can be applied to various HVAC control applications. The paper also explores the use of Artificial Neural Network methods for designing efficient controllers and demonstrates the effectiveness of the framework.
Article
Construction & Building Technology
Shuxun Li, Jianzheng Zhang, Jianjun Hou, Bohao Zhang, Lingxia Yang, Mingxing Zheng
Summary: Optimizing the control accuracy of pressure regulators is crucial for reducing energy consumption and improving indoor cooling and heating comfort in HVAC systems of buildings. This study used the transient dynamics calculation method to simulate the movement process of the regulator diaphragm and proposed an improved whale algorithm to optimize the Kriging model. The results showed that the optimized diaphragm had higher fitting accuracy, smaller prediction error, reduced rebound force by 13.13%, and increased fatigue life by 87.89%. The experiments confirmed the improved dynamic characteristics and control accuracy of the optimized diaphragm, resulting in a 2.03% reduction in maximum flow value, 15.1% reduction in transition time, and closer stable flow value to 20 t/h.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Construction & Building Technology
Qiming Fu, Zhu Li, Zhengkai Ding, Jianping Chen, Jun Luo, Yunzhe Wang, You Lu
Summary: In this study, a new event-driven Markov decision process (ED-MDP) framework and event-driven deep Q network (ED-DQN) method were developed to optimize residential HVAC control. Experimental results showed that the proposed ED-DQN achieved state-of-the-art performance in terms of energy saving and thermal comfort violations compared to benchmark methods and other RL methods.
BUILDING AND ENVIRONMENT
(2023)
Article
Engineering, Multidisciplinary
Samy Faddel, Qun Zhou Sun, Guanyu Tian
Summary: This paper presents a method to coordinate energy consumption in commercial buildings, taking into account privacy protection. By using convex formulation and alternating direction method of multiplier, coordination among buildings can be achieved while protecting privacy. The validation on a practical case shows that the method can achieve comparable performance to centralized control.
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Abida Sharif, Jian Ping Li, Muhammad Asim Saleem, Gunasekaran Manogran, Seifedine Kadry, Abdul Basit, Muhammad Attique Khan
Summary: The Internet of Vehicles (IoV) connects vehicles to the Internet to transfer information, and network clustering strategies are proposed to solve traffic management challenges in IoV networks. Reinforcement learning is used to learn optimal policies, and an experience-driven approach based on deep reinforcement learning is proposed for efficiently selecting cluster heads in managing network resources in the IoV environment.
JOURNAL OF INTELLIGENT MANUFACTURING
(2021)
Article
Engineering, Mechanical
L. Ben Said, L. Kolsi, N. Ben Khedher, F. Alshammari, E. H. Malekshah, A. K. Hussein
Summary: This study numerically investigates the fluid-structure interaction during CNT-water nanofluid mixed convection in a micro-channel with elastic fins. The results show that fin oscillations can reduce drag and lift forces, improve heat transfer and cooling, and the dispersion of CNT nanoparticles in water-based fluid significantly enhances the convection process.
EXPERIMENTAL TECHNIQUES
(2023)
Article
Computer Science, Artificial Intelligence
Leonardo Goliatt, Zaher Mundher Yaseen
Summary: In this research, a hybrid model combining Covariance Matrix Adaptive Evolution Strategies (CMAES) with Extreme Gradient Boosting (XGB) and Multi-Adaptive Regression Splines (MARS) was developed to predict daily solar radiation. The model was tested at four meteorological stations in Burkina Faso and showed outstanding predictability performance, achieving significantly higher accuracy compared to existing methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Engineering, Environmental
Zahirah Mohd Zain, Ahmed Saud Abdulhameed, Ali H. Jawad, Zeid A. ALOthman, Zaher Mundher Yaseen
Summary: This study developed a pH-sensitive chitosan/sepiolite clay/algae biocomposite for efficient removal of cationic and anionic dyes by adjusting the solution pH. The optimal conditions for dye removal were determined using a Box-Behnken design and the adsorption mechanism was investigated. The biocomposite exhibited high adsorption capacity and the adsorption process was confirmed to be endothermic and spontaneous. Various interactions, such as electrostatic, H-bonding, and n-pi interactions, contributed to the adsorption mechanism.
JOURNAL OF POLYMERS AND THE ENVIRONMENT
(2023)
Article
Engineering, Chemical
Yuguo Gao, Ihab M. T. A. Shigidi, Masood Ashraf Ali, Raad Z. Homod, Mohammad Reza Safaei
Summary: Using various machine learning methods, this study predicted the thermophysical properties of phase change materials (PCM) containing three nanoparticles. Experimental data from the literature were used to establish the relationship between PCM and nanoparticles. The regression algorithms used in this research accurately predicted the properties of the materials.
JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS
(2023)
Correction
Thermodynamics
Waqar Ahmed, Omer A. Alawi, Ali H. Abdelrazek, Zaher Mundher Yaseen, Mayadah W. Falah, Omar A. Hussein, Mahmoud Eltaweel, Raad Z. Homod, Nor Azwadi Che Sidik
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2023)
Article
Chemistry, Multidisciplinary
Husam Abdulrasool Hasan, Jenan S. Sherza, Azher M. Abed, Hussein Togun, Nidhal Ben Khedher, Kamaruzzaman Sopian, Jasim M. Mahdi, Pouyan Talebizadehsardari
Summary: This article investigates the influence of including transverse ribs within the absorber tube in the concentrated linear Fresnel collector system on thermal and flow performance coefficients. Simulations were conducted using water as the heat transfer fluid and the results showed that the inclusion of transverse ribs significantly enhanced heat transfer rates. The average Nusselt number increased by approximately 115% at Re = 5,000 and 175% at Re = 13,000 when compared to the plain absorber tube.
FRONTIERS IN CHEMISTRY
(2023)
Article
Energy & Fuels
Ahmed Kadhim Hussein, Mohammed El Hadi Attia, Husham Jassim Abdul-Ammer, Muesluem Arici, Mohamed Bechir Ben Hamida, Obai Younis, Raad Z. Homod, Awatef Abidi
Summary: In this research, low-cost energy storage materials were used to enhance the performance of single-slope solar distillers. The conventional distiller was modified by adding salt balls and sponges to the basin at different water depths. The findings showed that using these energy storage materials significantly increased the productivity of the modified solar distillers.
Article
Thermodynamics
Husam Abdulrasool Hasan, Hussein Togun, Azher M. Abed, Naef A. A. Qasem, Aissa Abderrahmane, Kamel Guedri, Sayed M. Eldin
Summary: Temperature has a significant impact on the efficiency, security, and cycle life of lithium-ion battery cells. A new cooling system with a specific fluid velocity range is used to reduce the temperature of the cells. The results show that higher fluid velocity leads to better heat transfer. Among the investigated nanofluids, SiO2 exhibits the best thermal cooling for battery packs.
CASE STUDIES IN THERMAL ENGINEERING
(2023)
Article
Energy & Fuels
Raad Z. Homod, Ghazwan Noori Saad Jreou, Hayder Ibrahim Mohammed, Amjad Almusaed, Ahmed Kadhim Hussein, Wael Al-Kouz, Hussein Togun, Muneer A. Ismael, Hussein Alawai Ibrahim Al-Saaidi, Omer A. Alawi, Zaher Mundher Yaseen
Summary: Due to the influence of nonlinearities and high delay time, a hybrid model combining white-box and black-box approaches is developed to handle the behavior of a complex oil field system in Iraq. This model effectively represents the large data sets and achieves optimal fitness to measured values. The conversion of fuzzy rules into a multilayer perceptron network and the use of clustering technique and Gauss-Newton regression enhance the predictive performance of the hybrid model.
GEOENERGY SCIENCE AND ENGINEERING
(2023)
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.
Review
Engineering, Chemical
Faical Khlissa, Mohsen Mhadhbi, Walid Aich, Ahmed Kadhim Hussein, Muapper Alhadri, Fatih Selimefendigil, Hakan F. Oztop, Lioua Kolsi
Summary: This research conducts a thorough study and assessment of phase-change materials, highlighting the latest developments in nanoencapsulated PCM technology and thermal energy storage. It also acknowledges the efforts made by researchers to improve the efficiency and efficacy of PCM, and discusses current challenges and future directions.
Review
Energy & Fuels
Farhan Lafta Rashid, Mudhar A. Al-Obaidi, Nabeel S. Dhaidan, Ahmed Kadhim Hussein, Bagh Ali, Mohamed Bechir Ben Hamida, Obai Younis
Summary: Latent heat energy storage using phase change materials (PCM) is an effective method for reducing energy usage. Bio-based phase change materials (BPCM) offer a renewable and environmentally-friendly alternative to traditional PCM. This paper reviews and discusses the choice, mechanisms, preparation, and applications of BPCMs in detail.
JOURNAL OF ENERGY STORAGE
(2023)
Article
Energy & Fuels
Raad Z. Homod, Hayder Ibrahim Mohammed, Aissa Abderrahmane, Omer A. Alawi, Osamah Ibrahim Khalaf, Jasim M. Mahdi, Kamel Guedri, Nabeel S. Dhaidan, A. S. Albahri, Abdellatif M. Sadeq, Zaher Mundher Yaseen
Summary: This study proposes a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adds a pre-cooling coil in the air handling unit (AHU) to alleviate the coupling issue in HVAC systems. The DCLTML algorithm shows promising results in controlling HVAC systems, with significant energy savings and improved environmental comfort.
Article
Environmental Sciences
Syed Shabi Ul Hassan Kazmi, Hafiz Sohaib Ahmed Saqib, Paolo Pastorino, Hans-Peter Grossart, Zaher Mundher, Muyassar H. Abualreesh, Wenhua Liu, Zhen Wang
Summary: This research investigates the influence of the veterinary antibiotic nitrofurazone on the community dynamics of marine periphytic ciliates. The results show significant alterations in ciliate communities following exposure to nitrofurazone, including changes in abundance, composition, and structure. These findings enhance the understanding of the ecological impacts of nitrofurazone on marine periphytic ciliate communities and highlight the need for vigilant monitoring of veterinary antibiotics to protect marine ecosystem health and biodiversity.
SCIENCE OF THE TOTAL ENVIRONMENT
(2023)
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.