Article
Energy & Fuels
Paria Movahed, Saman Taheri, Ali Razban
Summary: Long-term operation of HVAC systems can lead to failures, higher energy consumption, and maintenance costs. Fault detection diagnostic (FDD) is commonly used to prevent malfunctions, and machine learning methods have gained interest due to their high accuracy. However, existing studies suffer from biased classification algorithms and high false positives. To address these challenges and improve diagnostic performance, this study proposes a novel data-driven framework using principal component analysis, time series anomaly detection, and random forest.
Article
Energy & Fuels
Haoshan Ren, Chengliang Xu, Yuanli Lyu, Zhenjun Ma, Yongjun Sun
Summary: This study proposes a novel method that integrates thermodynamic laws with deep learning to address the challenging issue of sensor fault detection in large and complex HVAC systems. The integration enables the explicit learning of thermodynamic laws and reduces/eliminates unreasonable results frequently observed in sole deep learning methods. The proposed method improves the fault detection rate and reduces the false alarm rate, providing an effective and reliable means for ensuring sensor healthy operation in HVAC systems.
Article
Construction & Building Technology
Fanyong Cheng, Wenjian Cai, Xin Zhang, Huanyue Liao, Can Cui
Summary: This paper introduces a novel fault detection and diagnosis method using multiscale convolutional neural networks for Air Handling Unit in HVAC system. By utilizing three different scale kernels and an end-to-end learning strategy, the proposed method can effectively extract discriminative features to improve diagnostic performance. The comparison results show that the proposed MCNNs-based FDD method outperforms other commonly used methods.
ENERGY AND BUILDINGS
(2021)
Article
Chemistry, Analytical
Guannan Li, Haonan Hu, Jiajia Gao, Xi Fang
Summary: A dynamic calibration method based on Bayesian inference has been developed for sensor drift faults in HVAC systems. The results show that this method effectively improves the calibration accuracy of drift faults with high detection accuracy.
Article
Chemistry, Analytical
Hai Cao, Jinpeng Yu, Yu Wang, Liang Zhang, Jongwon Kim
Summary: This paper proposes a fault diagnosis system for pipeline robots based on sound signal recognition, which can timely and accurately identify fault types, reduce the probability of serious faults, and improve the reliability of pipeline robots.
Article
Construction & Building Technology
Guannan Li, Yue Zheng, Jiangyan Liu, Zhenxin Zhou, Chengliang Xu, Xi Fang, Qing Yao
Summary: The study proposed an improved Stacking sensor fault detection method using fault-discrimination information, employing ensemble learning with four single models to achieve better fault detection performance and lower false-alarm rate compared to traditional Stacking methods.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Construction & Building Technology
Zhimin Du, Kang Chen, Siliang Chen, Jinning He, Xu Zhu, Xinqiao Jin
Summary: The automation of fault diagnosis and smart management is crucial for the reliability of data centers' HVAC systems. Machine learning using historical data is an efficient method for developing smart management tools. However, the imbalanced training data, with more fault-free data than fault data, limits the diagnosis capacity of machine learning models. To address this issue, a deep learning-based generative adversarial network is proposed to integrate with an incremental learning SVM model to diagnose commonly occurred faults in data center air conditioning systems. The experimental results demonstrate that this approach achieves acceptable diagnosis accuracies.
ENERGY AND BUILDINGS
(2023)
Article
Construction & Building Technology
Kang Chen, Siliang Chen, Xu Zhu, Xinqiao Jin, Zhimin Du
Summary: This paper proposes an interpretable mechanism mining enhanced deep learning method for fault detection and diagnosis (FDD) model transfer among different HVAC systems. By conducting fault simulation experiments and training a one-dimensional convolutional neural network (1D-CNN), a general FDD model is obtained and verified on another type of chiller. The testing results indicate that the retrained transfer model has a good diagnostic effect for the target chiller.
BUILDING AND ENVIRONMENT
(2023)
Article
Computer Science, Information Systems
Rodrigo Marino, Cristian Wisultschew, Andres Otero, Jose M. Lanza-Gutierrez, Jorge Portilla, Eduardo de la Torre
Summary: This article presents a machine learning-based methodology for fault detection in continuous processes, using a hybrid feature selection approach to select the most representative sensors and achieve high-quality fault identification. The proposed technique follows a distributed approach, overcoming limitations of centralized methods. Experimental results show that the methodology provides state-of-the-art detection quality for TEP fault-detection, with significantly lower latency and feature usage compared to other implementations. The scalability of the framework allows for optimal implementation selection based on application needs.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Mustafa Musa Jaber, Mohammed Hasan Ali, Sura Khalil Abd, Mustafa Mohammed Jassim, Ahmed Alkhayyat, Mohammed Sh. Majid, Ahmed Rashid Alkhuwaylidee, Shahad Alyousif
Summary: This paper proposes a ResNet-based deep learning multilayer fault detection model (ResNet-DLMFDM) to enhance high performance, design, and transfer learning skills. Experimental results show that the proposed approach outperforms current algorithms in terms of data correctness, storage space utilization, computational complexity, noiselessness, and transfer performance.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Jinxin Wang, Xiuquan Sun, Chi Zhang, Xiuzhen Ma
Summary: This paper proposes a system-level fault diagnosis methodology based on fault behavior analysis, optimal sensor placement, and intelligent data analytics for multiple fault detection and isolation. By constructing a dynamical model and using set partitioning theory, a condition monitoring system with optimal sensor placement can be designed, and multivariate statistic measures are used to detect potential faults.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Chemistry, Analytical
Iva Matetic, Ivan Stajduhar, Igor Wolf, Sandi Ljubic
Summary: This review paper presents and systematizes cutting-edge FDD methodologies for HVAC systems, providing best-practice heuristics for researchers and solution developers in this domain.
Article
Chemistry, Analytical
Wenbo Na, Siyu Guo, Yanfeng Gao, Jianxing Yang, Junjie Huang
Summary: A real-time fault diagnosis method based on data-driven is proposed in this study for the single fault of the second-order valued system sensors. Static fault detection, location, estimation, and separation models are established using off-line data, and calibrated with on-line data to obtain real-time fault diagnosis models. The experiments results show the validity and accuracy of the proposed method, which is suitable for the general cascade system.
Article
Engineering, Electrical & Electronic
Shengli Zhao, Lizhi Zhang, Liyun Su
Summary: This paper addresses the problem of signal detection under chaotic noise in a distributed detection fusion system, proposing a new mechanism that effectively detects weak signals under chaotic noise background and outperforms local sensors with low SNR in fusion performance.
JOURNAL OF SENSORS
(2021)
Article
Engineering, Civil
Xueming Li, Jiamin Xu, Zhiwen Chen, Shaolong Xu, Kan Liu
Summary: This paper proposes a real-time fault diagnosis method for the impulse rectifier of a traction system, which establishes a structural model of interest through structural analysis and optimization, achieves fault isolation using minimum structural overdetermined sets, and makes diagnosis decisions using the CUSUM algorithm. The effectiveness of the proposed method is verified through hardware testing, demonstrating good feasibility and accuracy.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Energy & Fuels
Hong Tang, Shengwei Wang
Summary: This study develops a novel model-based predictive dispatch strategy for hybrid building energy systems to maximize economic benefits in electricity markets by considering the correlation between multiple flexibility resources and grid control signals, which can lower electricity costs in the electricity market.
Article
Energy & Fuels
Wenzhuo Li, Shengwei Wang
Summary: This paper proposes a fully distributed optimal control approach for HVAC systems to be implemented in IoT-enabled building automation networks. It utilizes the Incremental Cost Consensus (ICC) algorithm and the average consensus algorithm to optimize the individual rooms' outdoor air volume and estimate the outdoor air volume mismatch. Through tests and comparisons, it is found that the proposed approach with the fully connected topology outperforms existing hierarchical distributed approaches, demonstrating higher robustness, lower computation complexity, and higher optimization efficiency.
Article
Thermodynamics
Shaobo Sun, Shengwei Wang, Kui Shan
Summary: This study quantifies the measurement uncertainties of water flow meters in multiple water-cooled chiller systems using a Bayesian approach, proposing a quantification strategy that performs well in quantifying both systematic and random uncertainties with acceptable accuracy. The strategy can be used to optimize the control of chiller systems and improve their reliability.
APPLIED THERMAL ENGINEERING
(2022)
Article
Thermodynamics
Hangxin Li, Shengwei Wang
Summary: Constructing nearly and net zero-energy buildings presents the challenge of achieving energy goals post-occupancy, with traditional design methods showing high risk of failure. New ZEB design standards now focus on post-occupancy performance evaluation, posing additional challenges and calling for effective design methods.
BUILDING SIMULATION
(2022)
Article
Thermodynamics
Hangxin Li, Shengwei Wang
Summary: Model predictive control (MPC) method is superior in enhancing system performance, but is hindered by forecast uncertainties in operation. Different methods, such as shrinking horizon MPC (SHMPC) and stochastic MPC (SMPC), have been proposed to mitigate these uncertainties. However, there is limited knowledge about their year-round performance and relative performance, especially in the utilization of building flexibility-resources.
Article
Energy & Fuels
Shaobo Sun, Kui Shan, Shengwei Wang
Summary: This study proposed an online robust sequencing control strategy for chiller plants under low-quality and uncertain flow measurements, which effectively reduced the impacts of flow measurement uncertainties and improved the performance of chiller plants. The uncertainty processing model accurately quantified the measurement uncertainties of water flow rates, leading to significant reductions in root-mean-square error of cooling loads, total switching number of chillers, and cumulative unmet cooling load. The proposed control strategy showed the ability to tolerate flow measurement uncertainties.
Article
Construction & Building Technology
Hanbei Zhang, Fu Xiao, Chong Zhang, Rongling Li
Summary: This study develops a coordinated optimal load scheduling strategy for building cluster load management that considers dynamic electricity price and marginal emission factor simultaneously. The strategy effectively solves the conflicts of minimizing electricity cost, carbon emissions, and peak load while ensuring user satisfaction. The results show that the strategy can achieve a compromise between conflicting objectives in different correlation scenarios.
ENERGY AND BUILDINGS
(2023)
Article
Thermodynamics
Zhijie Chen, Fangzhou Guo, Fu Xiao, Xiaoyu Jin, Jian Shi, Wanji He
Summary: This study developed a data-driven benchmarking methodology to detect anomalous operations with degraded energy performance from a large number of bus ACs. By using a Long-Short-Term-Memory (LSTM) autoencoder-based similarity measurement method, similar operation data in other ACs can be identified for benchmarking. A LSTM network-based reference model is then developed to predict the power consumption of the target AC using these similar data. Statistical analysis-based trend and change detection algorithms are used to identify anomalies in power consumption ratio (PCR) over a few days. Two fault experiments were conducted to validate the methodology, showing its potential for health monitoring of bus ACs in a city fleet.
INTERNATIONAL JOURNAL OF REFRIGERATION
(2023)
Article
Engineering, Electrical & Electronic
Zhuang Zheng, Shengwei Wang, Wenzhuo Li, Xiaowei Luo
Summary: This paper proposes a novel voltage control strategy that regulates the on/off states of AC clusters to address voltage issues caused by high PV penetrations. The strategy includes temperature priority-based on/off control, real-time optimal demand response resources dispatch, distributed sensing of ACs, and flexibility capacity estimation. The strategy is validated to be effective and scalable, and is incorporated into a hierarchical control framework for smart grid voltage control.
IEEE TRANSACTIONS ON SMART GRID
(2023)
Article
Construction & Building Technology
Tianhang Zhang, Zilong Wang, Yanfu Zeng, Xiqiang Wu, Xinyan Huang, Fu Xiao
Summary: A novel framework of Artificial-Intelligence Digital Fire (AID-Fire) was proposed for real-time identification of building fire evolution, showing promising results in a full-scale fire test room.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Construction & Building Technology
Wenxuan Zhao, Hangxin Li, Shengwei Wang
Summary: This study proposes a novel outdoor air ventilation strategy for high-tech cleanrooms, which enables maximum energy savings and efficiency. The strategy determines the optimal outdoor air volume by theoretically calculating the energy differential. Results show that the traditional fully coupled AHU system can achieve annual free cooling hours ranging from 662 to 2,537 in 31 major Chinese cities. Moreover, the proposed strategy achieves 8% energy savings in transition months and significant electricity and primary energy savings in a year.
BUILDING AND ENVIRONMENT
(2023)
Review
Construction & Building Technology
Xiaoyu Jin, Chong Zhang, Fu Xiao, Ao Li, Clayton Miller
Summary: Data related to building energy use is crucial for research and applications in building energy efficiency. However, most cities lack comprehensive and publicly accessible building energy use datasets, hindering urban building energy modeling, energy planning, performance benchmarking, and policymaking. This review paper provides insights based on a comprehensive analysis of worldwide open datasets and their applications, including detailed information on 33 building energy datasets and their subdomains, as well as proposed policy implications. The review also discusses privacy solutions and offers valuable conclusions to support city-level building energy data disclosure, modeling, and policymaking.
ENERGY AND BUILDINGS
(2023)
Article
Energy & Fuels
Ao Li, Chong Zhang, Fu Xiao, Cheng Fan, Yang Deng, Dan Wang
Summary: Data-driven models are widely used in smart building energy management. This paper investigates the performance of three conventional model update methods and five emerging continual learning methods using a 2-year dataset. The results show that continual learning methods are more effective in ensuring long-term accuracy while reducing computation time and data storage expenses.
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
Computer Science, Artificial Intelligence
Xinqi Zhang, Jihao Shi, Xinyan Huang, Fu Xiao, Ming Yang, Jiawei Huang, Xiaokang Yin, Asif Sohail Usmani, Guoming Chen
Summary: This study proposes a deep probabilistic graph neural network that models the spatial dependency of sensors to improve leakage detection. The results demonstrate that the model achieves competitive detection accuracy and provides more comprehensive leakage detection information. Additionally, the model's posterior distribution enhances leakage localization accuracy.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Construction & Building Technology
Samiran Khorat, Debashish Das, Rupali Khatun, Sk Mohammad Aziz, Prashant Anand, Ansar Khan, Mattheos Santamouris, Dev Niyogi
Summary: Cool roofs can effectively mitigate heatwave-induced excess heat and enhance thermal comfort in urban areas. Implementing cool roofs can significantly improve urban meteorology and thermal comfort, reducing energy flux and heat stress.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Qi Li, Jiayu Chen, Xiaowei Luo
Summary: This study focuses on the vertical wind conditions as a main external factor that limits the energy assessment of high-rise buildings in urban areas. Traditional tools for energy assessment of buildings use a universal vertical wind profile estimation, without taking into account the unique wind speed in each direction induced by the various shapes and configurations of buildings in cities. To address this limitation, the study developed an omnidirectional urban vertical wind speed estimation method using direction-dependent building morphologies and machine learning algorithms.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Xiaojun Luo, Lamine Mahdjoubi
Summary: This paper presents an integrated blockchain and machine learning-based energy management framework for multiple forms of energy allocation and transmission among multiple domestic buildings. Machine learning is used to predict energy generation and consumption patterns, and the proposed framework establishes optimal and automated energy allocation through peer-to-peer energy transactions. The approach contributes to the reduction of greenhouse gas emissions and enhances environmental sustainability.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ying Yu, Yuanwei Xiao, Jinshuai Chou, Xingyu Wang, Liu Yang
Summary: This study proposes a dual-layer optimization design method to maximize the energy sharing potential, enhance collaborative benefits, and reduce the storage capacity of building clusters. Case studies show that the proposed design significantly improves the performance of building clusters, reduces energy storage capacity, and shortens the payback period.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Felix Langner, Weimin Wang, Moritz Frahm, Veit Hagenmeyer
Summary: This paper compares two main approaches to consider uncertainties in model predictive control (MPC) for buildings: robust and stochastic MPC. The results show that compared to a deterministic MPC, the robust MPC increases the electricity cost while providing complete temperature constraint satisfaction, while the stochastic MPC slightly increases the electricity cost but fulfills the thermal comfort requirements.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Somil Yadav, Caroline Hachem-Vermette
Summary: This study proposes a mathematical model to evaluate the performance of a Double Skin Facade (DSF) system and its impact on indoor conditions. The model considers various design parameters and analyzes their effects on the system's electrical output and room temperature.
ENERGY AND BUILDINGS
(2024)
Article
Construction & Building Technology
Ruijun Chen, Holly Samuelson, Yukai Zou, Xianghan Zheng, Yifan Cao
Summary: This research introduces an innovative resilient design framework that optimizes building performance by considering a holistic life cycle perspective and accounting for climate projection uncertainties. The study finds that future climate scenarios significantly impact building life cycle performance, with wall U-value, windows U-value, and wall density being major factors. By using ensemble learning and optimization algorithms, predictions for carbon emissions, cost, and indoor discomfort hours can be made, and the best resilient design scheme can be selected. Applying this framework leads to significant improvements in building life cycle performance.
ENERGY AND BUILDINGS
(2024)