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
Energy & Fuels
Amirreza Heidari, Francois Marechal, Dolaana Khovalyg
Summary: This study proposes a control framework based on Reinforcement Learning to integrate occupants' stochastic behavior into hot water systems control, achieving a balance between water hygiene, comfort, and energy use. The framework successfully adapts to occupants' behavior and achieves significant energy savings, while maintaining occupants' comfort and water hygiene.
Article
Construction & Building Technology
Haneul Choi, Bonghoon Jeong, Joosang Lee, Hooseung Na, Kyungmo Kang, Taeyeon Kim
Summary: This study presents a method using deep learning and computer vision to estimate the real-time metabolic rate and clothing insulation of individuals. It also proposes a comfort temperature control strategy based on these characteristics, which has been successfully implemented and achieved satisfactory results.
BUILDING AND ENVIRONMENT
(2022)
Article
Thermodynamics
Yanxue Li, Zixuan Wang, Wenya Xu, Weijun Gao, Yang Xu, Fu Xiao
Summary: An efficient and flexible energy management strategy is crucial for energy conservation in the building sector. This study proposes a hybrid model-based reinforcement learning framework that uses short-term monitored data to optimize indoor thermal comfort and energy cost-saving performance. Simulation results demonstrate the efficiency and superiority of the proposed framework, with the D3QN agent achieving over 30% cost savings compared to measurement results.
Article
Construction & Building Technology
Zu Wang, John Calautit, Paige Wenbin Tien, Shuangyu Wei, Wuxia Zhang, Yupeng Wu, Liang Xia
Summary: Occupant-Centric Control strategies have gained interest in adjusting building systems, but its application to natural ventilation systems and its impact on air quality have been overlooked. This study proposes an Occupant-Centric Heating and Natural Ventilation Control strategy that utilizes real-time occupant behavior data to improve thermal comfort, reduce energy consumption, and enhance indoor air quality. The strategy showed significant improvements in energy consumption, thermal comfort, and CO2 concentration compared to conventional control strategies.
ENERGY AND BUILDINGS
(2023)
Review
Construction & Building Technology
Haneul Choi, Chai Yoon Um, Kyungmo Kang, Hyungkeun Kim, Taeyeon Kim
Summary: This study conducted a comprehensive literature review on vision-based occupant information systems, proposing a five-tier taxonomy, presenting a systematic summary, reviewing the performance of sensing systems, analyzing the applicability of deep-learning techniques, summarizing privacy-preservation techniques, and providing control strategies and energy saving potential analysis. The analysis in this review contributes significantly towards addressing challenges in the research field.
BUILDING AND ENVIRONMENT
(2021)
Article
Construction & Building Technology
Canjun Li, Han Zhu, Xiangchao Lian, Yuxin Liu, Xiaohan Li, Yanbo Feng
Summary: To achieve occupant-centric building and control, it is important to consider occupant behavior characteristics and develop operational strategies accordingly. By studying the time-lag of shading behavior, an advanced prediction model was proposed, improving prediction accuracy. Furthermore, an operational logic that meets energy savings and comfort requirements was derived by describing the dynamic distribution of office room occupancy.
BUILDING AND ENVIRONMENT
(2022)
Article
Computer Science, Artificial Intelligence
Zheng-Kai Ding, Qi-Ming Fu, Jian-Ping Chen, Hong-Jie Wu, You Lu, Fu-Yuan Hu
Summary: This paper proposes a deep reinforcement learning-based thermal comfort control method for multi-zone residential HVAC systems. By designing an SVR-DNN model and applying the optimization strategy based on Deep Deterministic Policy Gradient (DDPG), the method minimizes energy consumption while satisfying occupants' thermal comfort.
CONNECTION SCIENCE
(2022)
Article
Transportation Science & Technology
Yuchuan Du, Jing Chen, Cong Zhao, Chenglong Liu, Feixiong Liao, Ching-Yao Chan
Summary: Crowdsourced data can enhance driving performance of autonomous vehicles on rough pavements by controlling speed to address discomfort and inefficiency issues. The study introduces the concept of 'maximum comfortable speed' and designs a deep reinforcement learning algorithm to learn comfortable and energy-efficient speed control strategies.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Construction & Building Technology
Yuting An, Chun Chen
Summary: This study developed a controller using deep reinforcement learning (DRL) to reduce indoor PM2.5 pollution and maintain thermal comfort with low energy consumption. The controller was trained using the deep Q-network (DQN) algorithm and successfully controlled the window, air cleaner, and air conditioner in a real room. Compared to a baseline controller, the DQN controller increased the PM2.5 healthy period and thermal comfort period by around 21% and 16% respectively, while reducing energy consumption by 23%. Additionally, simulations showed the effectiveness of the DQN controller in other rooms with different characteristics.
ENERGY AND BUILDINGS
(2023)
Article
Construction & Building Technology
Maohui Luo, Qichun Zheng, Ye Zhao, Fei Zhao, Xiang Zhou
Summary: This study introduces the design and benefits of occupant-centric smart thermostats (OCST) in improving energy efficiency and thermal comfort. Through data analysis, it is shown that OCST can significantly reduce energy consumption in single-family houses.
ENERGY AND BUILDINGS
(2023)
Article
Construction & Building Technology
Nidia Bucarelli, Nora El-Gohary
Summary: This paper proposes a novel non-invasive approach for automated thermal discomfort detection using a deep learning-based model. The model recognizes nine discomfort cues from video recordings of occupants in indoor work settings, which could be associated with the cold or warm thermal discomfort states. This approach can provide rapid and effortless feedback in building energy management systems to reduce energy waste while providing comfortable indoor conditions.
BUILDING AND ENVIRONMENT
(2023)
Article
Thermodynamics
Jingsi Zhang, Xiang Zhou, Song Lei, Maohui Luo
Summary: The Personal Comfort System (PCS) aims to enhance thermal comfort satisfaction and reduce air conditioning energy consumption. Through experiments, simulations, and energy analysis, it has been shown that PCS can significantly improve occupants' thermal comfort and reduce energy consumption by 25%-40%.
BUILDING SIMULATION
(2022)
Article
Construction & Building Technology
Bin Yang, Yihang Liu, Pengju Liu, Faming Wang, Xiaogang Cheng, Zhihan Lv
Summary: This study used a deep learning-based computer vision method for indoor occupancy detection and proposed an occupant-centric control strategy based on the monitored occupant number to regulate supply air parameters and outdoor air volume for energy saving purposes. The results showed that compared to the traditional control strategy, the proposed strategy improved comfort by 43%-73%, maintained acceptable air quality, kept CO2 concentration below 700 ppm, and saved energy by 2.3%-8.1%. It was also found that the lower the occupancy, the greater the improvement in comfort and energy savings.
BUILDING AND ENVIRONMENT
(2023)
Article
Construction & Building Technology
Nour Haidar, Nouredine Tamani, Yacine Ghamri-Doudane, Alain Boujou
Summary: Optimizing building energy consumption is crucial for reducing environmental impact. Information technology can be used to deploy sensors in buildings and collect data on energy consumption and occupant behavior. A graph mining-based optimization method that combines behavior prediction and reinforcement learning is introduced to predict user behavior, detect errors, and refine the model.
BUILDING AND ENVIRONMENT
(2023)
Article
Construction & Building Technology
Sicheng Zhan, Bing Dong, Adrian Chong
Summary: Building energy flexibility is crucial for improving local renewable energy consumption and building self-sufficiency. However, the study of building energy flexibility in the tropical region is limited. This study proposes a practical control framework based on Model Predictive Control (MPC) to uncover the energy flexibility potential of a tropical office building. The effects of data availability on control performance are also investigated. The results show that accurate local weather data is critical for desirable control results, and higher data granularity can benefit control performance under different building characteristics.
ENERGY AND BUILDINGS
(2023)
Article
Construction & Building Technology
Wenxin Li, Takamasa Hasama, Adrian Chong, Joo Guan Hang, Bertrand Lasternas, Khee Poh Lam, Kwok Wai Tham
Summary: Ceiling fans have been found to effectively reduce the transmission of droplets and aerosols during coughing by diluting the concentration of particles in the air. They can change the airflow pattern and provide better protection against cough exposure.
BUILDING AND ENVIRONMENT
(2023)
Article
Construction & Building Technology
Xilei Dai, Siyu Cheng, Adrian Chong
Summary: In this study, reinforcement learning (RL) is used to apply mixed-mode ventilation (MMV) in the tropics, aiming to reduce cooling energy consumption while maintaining comfort. Results show that RL can achieve a 52% reduction in cooling energy with good comfort and indoor air quality. Additionally, an Explainable AI (XAI) framework is developed to extract control rules and reveal the mechanism of using internal thermal mass to improve cooling efficiency.
ENERGY AND BUILDINGS
(2023)
Article
Energy & Fuels
Dian Zhuang, Vincent J. L. Gan, Zeynep Duygu Tekler, Adrian Chong, Shuai Tian, Xing Shi
Summary: This study proposes a data-driven predictive control method using time-series forecasting and reinforcement learning to optimize HVAC operations. By analyzing various sensor metadata, it achieves 17.4% energy savings and 16.9% thermal comfort improvement. The results show that recursive prediction reduces model accuracy significantly, while the attention mechanism improves prediction performance.
Article
Construction & Building Technology
Martin Mosteiro-Romero, Clayton Miller, Adrian Chong, Rudi Stouffs
Summary: The COVID-19 pandemic has accelerated the increase in workplace flexibility, and this paper discusses how demand-driven control strategies in the built environment can support this transition. By simulating different scenarios, the study finds that the implementation of occupancy-driven building controls can result in a decrease in space cooling demand of up to 17-63% due to employee absenteeism.
BUILDING AND ENVIRONMENT
(2023)
Article
Construction & Building Technology
Zeynep Duygu Tekler, Yue Lei, Yuzhen Peng, Clayton Miller, Adrian Chong
Summary: In this study, a hybrid active learning framework was proposed to reduce data collection costs for developing efficient and robust personal comfort models. Two active learning algorithms and two labelling strategies were evaluated to achieve optimal reduction in user labelling effort. The results showed significant reduction in labelling effort for thermal comfort and air movement preference models, with increasing reductions over time and with new users. This study highlights the potential of active learning as an effective solution for the high cost of data collection in data-driven thermal comfort modelling.
BUILDING AND ENVIRONMENT
(2023)
Article
Construction & Building Technology
Yuzhen Peng, Nogista Antanuri, Siu-Kit Lau, Bahador Jebelli, Steve Kardinal Jusuf, Clayton Miller, Yi Ting Teo, Yun Xuan Chua, Adrian Chong
Summary: Mixed-mode buildings utilize both natural ventilation and mechanical air-conditioning systems to achieve energy savings without compromising occupant comfort. However, there is a lack of clear guidelines for assessing occupants' thermal perception and the interaction effects between thermal and acoustic comfort in these buildings. This study conducted a field study in a mixed-mode office building in Singapore and found that the operation modes and traffic noise significantly affect thermal sensation and acceptability. The findings suggest the need for a different thermal comfort model and provide valuable information for the practical operation of mixed-mode buildings.
BUILDING AND ENVIRONMENT
(2023)
Article
Construction & Building Technology
Vasantha Ramani, Miguel Martin, Pandarasamy Arjunan, Adrian Chong, Kameshwar Poolla, Clayton Miller
Summary: This study characterizes the air-conditioning usage pattern of non-residential buildings using longitudinal thermal images. The operational pattern of two different air-conditioning systems (water-cooled systems and window AC units) is analyzed from the thermal images. A difference in temperature changes between the window and wall is observed for water-cooled systems, while wavelet transform is used to extract frequency and time domain information for window AC units. The analysis results are compared to indoor temperature sensors, providing insights into HVAC system behavior without extensive sensor deployment.
ENERGY AND BUILDINGS
(2023)
Article
Thermodynamics
Zhihua Chen, Zhang Deng, Adrian Chong, Yixing Chen
Summary: This study developed a rapid building modeling tool AutoBPS-BIM, which can transfer BIM to BEM for load calculation and chiller design optimization. It demonstrated that the dynamic method reduced 33% of the chiller design capacity compared with the static load calculation. The study also showed that selecting the proper chiller number with different capacities is critical to achieving lower energy consumption, which achieved a 12.6% cooling system energy consumption reduction for the case study building.
BUILDING SIMULATION
(2023)
Article
Construction & Building Technology
Yaonan Gu, Wei Tian, Chao Song, Adrian Chong
Summary: Bayesian calibration of building energy simulation (BES) using expert knowledge can reduce uncertainties and improve the agreement between simulated and measured results. This study investigates the effects of output and parameter correlations, as well as data informativeness on the calibration performance of single-output Bayesian calibration (SOBC) and multiple-output Bayesian calibration (MOBC) for BES.
ENERGY AND BUILDINGS
(2023)
Review
Green & Sustainable Science & Technology
Yasaman Balali, Adrian Chong, Andrew Busch, Steven O'Keefe
Summary: Implementing an efficient control strategy for HVAC systems can improve energy efficiency and thermal performance in buildings. Researchers have extensively investigated the effectiveness of data-driven and model-based control methods, but precision and data quality remain challenges for practical implementation. This study aims to provide an overview of thermal modelling strategies, the state-of-the-art of control techniques, and the data requirements for thermal models. Unified guidelines and accurate prediction of human behavior and occupancy patterns are needed for practical implementation. Combining data-driven and physics-based models can help balance thermal comfort and energy efficiency in HVAC systems, but further research is needed to compare MPC and RL approaches and accurately measure the impact of human behavior and occupancy.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Construction & Building Technology
Yue Lei, Zeynep Duygu Tekler, Sicheng Zhan, Clayton Miller, Adrian Chong
Summary: Mixed-mode ventilation is a promising solution for achieving energy-efficient and comfortable indoor environments. This study found that occupants can thermally adapt when switching between natural ventilation (NV) and air-conditioning (AC) modes within the same day, with the adaptation process stabilizing between 35 to 45 minutes after the mode switch. These findings are important for optimizing thermal comfort in mixed-mode controls, considering the dynamic nature of thermal adaptation.
BUILDING AND ENVIRONMENT
(2024)
Article
Construction & Building Technology
Long Zheng, Adrian Chong, Hee Joo Poh, Chandra Sekhar
Summary: This study assessed the impact of building porosity on exterior convective heat transfer using experimental and computational fluid dynamics techniques. The findings revealed significant differences in exterior convective heat transfer coefficients between actual porous buildings and idealized porous buildings, highlighting the importance of accurate prediction of cooling load in building energy simulation.
BUILDING AND ENVIRONMENT
(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.