Article
Thermodynamics
Ying Sun, Fariborz Haghighat, Benjamin C. M. Fung
Summary: Data-driven models in the building domain have attracted much attention, but fairness-aware prediction of these models is a new research problem addressed in this paper. Different fairness definitions and pre-processing methods are introduced to improve fairness Type I and Type II while maintaining predictive accuracy. Sequential sampling is found to be a good option for improving fairness Type II with an acceptable decrease in accuracy.
Article
Construction & Building Technology
Ying Sun, Benjamin C. M. Fung, Fariborz Haghighat
Summary: This study proposes four in-processing methods to improve the predictive fairness of regression models in terms of having similar predictive performance between different conditions. The results show that the mean square error constrained (MSEC) method is the most effective in improving fairness, while the mean square error penalized (MSEP) method is another good option without significantly decreasing the overall accuracy. The mean residual difference constrained (MRDC) method effectively improves the similarity of absolute mean residual difference between different conditions, while the mean residual difference penalized (MRDP) method does not affect the predictive result.
ENERGY AND BUILDINGS
(2022)
Article
Computer Science, Artificial Intelligence
Zoey Liu, Emily Prud'hommeaux
Summary: Common model evaluation designs may not be applicable in low-resource crosslinguistic scenarios. This study explores model generalizability in such settings and finds that it depends on data set characteristics rather than data set size.
TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
(2022)
Article
Engineering, Civil
Zifeng Guo, Vahid Moosavi, Joao P. P. Leitao
Summary: This study explores the generalizability potential of convolutional neural networks (CNNs) as flood prediction models. The proposed CNN-based model can be reused in different catchment areas with different topography, and it predicts accurately on unseen catchment areas with significantly less computational time. The patch-based option is found to be more effective than the resizing-based option in terms of prediction accuracy.
JOURNAL OF HYDROLOGY
(2022)
Article
Construction & Building Technology
Bingqian Chen, Sumei Liu, Junjie Liu, Nan Jiang, Qingyan Chen
Summary: The study developed a data-driven RANS model to better predict the distributions of air velocity, temperature, and turbulent kinetic energy for indoor anisotropic flows by accurately simulating the nonlinear terms using an artificial neural network. The model showed reasonably good generalizability, indicating its potential for correctly predicting indoor anisotropic flows.
Article
Energy & Fuels
Ziqing Wei, Fukang Ren, Yikang Zhu, Bao Yue, Yunxiao Ding, Chunyuan Zheng, Bin Li, Xiaoqiang Zhai
Summary: This paper proposes a two-step identification process based on the resistance-capacity model to assess the reasonableness of the thermal characteristics of buildings for efficient operation.
Review
Green & Sustainable Science & Technology
Anjukan Kathirgamanathan, Mattia De Rosa, Eleni Mangina, Donal P. Finn
Summary: Managing supply and demand in the electricity grid becomes more challenging with the increasing penetration of variable renewable energy sources. Buildings are expected to have an expanding role in the future smart grid through better grid integration and predictive control. Data-driven predictive control, coupled with the Internet of Things, holds promise for scalable and transferrable approaches in grid integration of buildings.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Biology
Kaue de Sousa, Jacob van Etten, Jesse Poland, Carlo Fadda, Jean-Luc Jannink, Yosef Gebrehawaryat Kidane, Basazen Fantahun Lakew, Dejene Kassahun Mengistu, Mario Enrico Pe, Svein Oivind Solberg, Matteo Dell'Acqua
Summary: The study introduces a data-driven decentralized crop breeding approach called 3D-breeding, which aims to improve yields for smallholder farmers and demonstrates higher prediction accuracies for grain yield in a durum wheat case study in Ethiopia. 3D-breeding doubles the prediction accuracy of the benchmark, identifying genotypes with enhanced local adaptation for superior productive performance across seasons. This decentralized approach leverages the diversity in farmer fields to enhance local adaptation in challenging crop production environments.
COMMUNICATIONS BIOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Qiang Zhang, Yaming Zheng, Qiangqiang Yuan, Meiping Song, Haoyang Yu, Yi Xiao
Summary: This technical review examines the problem of mixed noise pollution in hyperspectral imaging (HSI), providing analysis of noise in different noisy HSIs and discussing crucial points for programming HSI denoising algorithms. It presents a general HSI restoration model for optimization and comprehensively reviews existing HSI denoising methods, including model-driven, data-driven, and model-data-driven strategies. The advantages and disadvantages of each strategy are summarized and contrasted, and evaluation of denoising methods is provided using simulated and real experiments. The review also presents prospects for future HSI denoising methods.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Multidisciplinary Sciences
Xiaojuan Li, Chen Wang, Mukhtar A. Kassem, Samuel Bimenyimana
Summary: This paper constructs an evolutionary game model between the government and prefabricated building developers to analyze developers' energy-saving and emission-reduction behavior. The study suggests that the government should combine positive and negative incentives and take into account developers' fairness preferences and profitability in setting incentives and fees. The paper provides suggestions for the government to improve targeted incentive policies and contribute to the promotion of prefabricated buildings.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Computer Science, Interdisciplinary Applications
Joni Salminen, Kamal Chhirang, Soon-Gyo Jung, Saravanan Thirumuruganathan, Kathleen W. Guan, Bernard J. Jansen
Summary: Creating user segments from data can lead to biased and inconsistent results. Comparing different algorithms, it is found that there is a trade-off between diversity and fairness. The choice of algorithm has a significant impact on how decision makers perceive the user population.
Article
Energy & Fuels
Jie Zhu, Jide Niu, Zhe Tian, Ruoyu Zhou, Chuang Ye
Summary: This paper proposes a framework for rapidly quantifying the demand response potential of buildings and their energy systems. By utilizing detailed co-simulation and data-driven methods, the demand response potential of buildings under different meteorological conditions and control strategies can be accurately captured, and the framework is easy to apply in practice.
Article
Computer Science, Artificial Intelligence
Penghui Lin, Limao Zhang, Jian Zuo
Summary: This paper presents an adaptive multi-model fusion approach for predicting building energy consumption, which combines clustering and XGBoost algorithm for training sub-models, and fusion with weighting and screening algorithm, resulting in improved accuracy of energy consumption prediction.
APPLIED SOFT COMPUTING
(2022)
Article
Multidisciplinary Sciences
Sebastian Scher, Simone Kopeinik, Andreas Truegler, Dominik Kowald
Summary: The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This paper uses statistics, data-driven approaches, and dynamical modeling to assess the long-term fairness effects of labor market interventions.
SCIENTIFIC REPORTS
(2023)
Article
Construction & Building Technology
Pandarasamy Arjunan, Kameshwar Poolla, Clayton Miller
Summary: This paper presents the design and implementation of BEEM, a data-driven energy use benchmarking system for buildings in Singapore. The system establishes peer groups for comparison using a public energy disclosure data set and utilizes an ensemble tree algorithm to accurately model building energy use and identify influential factors. Compared to baseline linear regression models used in the previous energy efficiency labeling program in Singapore and other recent models, our models significantly reduce prediction error. Using the prototype implementation of BEEM, we benchmarked and compared the energy performance of office, hotel, and retail buildings.
ENERGY AND BUILDINGS
(2022)
Article
Thermodynamics
Zhengxuan Liu, Zhun (Jerry) Yu, Tingting Yang, Mohamed El Mankibi, Letizia Roccamena, Ying Sun, Pengcheng Sun, Shuisheng Li, Guoqiang Zhang
ENERGY CONVERSION AND MANAGEMENT
(2019)
Review
Construction & Building Technology
Ying Sun, Fariborz Haghighat, Benjamin C. M. Fung
ENERGY AND BUILDINGS
(2020)
Article
Construction & Building Technology
Jianing (Tom) Luo, Mahmood Mastani Joybari, Karthik Panchabikesan, Ying Sun, Fariborz Haghighat, Alain Moreau, Miguel Robichaud
SUSTAINABLE CITIES AND SOCIETY
(2020)
Article
Thermodynamics
Ying Sun, Fariborz Haghighat, Benjamin C. M. Fung
Summary: Data-driven models in the building domain have attracted much attention, but fairness-aware prediction of these models is a new research problem addressed in this paper. Different fairness definitions and pre-processing methods are introduced to improve fairness Type I and Type II while maintaining predictive accuracy. Sequential sampling is found to be a good option for improving fairness Type II with an acceptable decrease in accuracy.
Article
Construction & Building Technology
Ying Sun, Karthik Panchabikesan, Fariborz Haghighat, Jianing (Tom) Luo, Alain Moreau, Miguel Robichaud
Summary: This study developed two advanced controllers to achieve peak shifting and heating cost-saving by controlling electrically heated floor and heat extraction system. The results showed that the developed controllers effectively shift energy consumption from the peak period and decrease heating cost by around 33%.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Construction & Building Technology
Ying Sun, Benjamin C. M. Fung, Fariborz Haghighat
Summary: This study proposes four in-processing methods to improve the predictive fairness of regression models in terms of having similar predictive performance between different conditions. The results show that the mean square error constrained (MSEC) method is the most effective in improving fairness, while the mean square error penalized (MSEP) method is another good option without significantly decreasing the overall accuracy. The mean residual difference constrained (MRDC) method effectively improves the similarity of absolute mean residual difference between different conditions, while the mean residual difference penalized (MRDP) method does not affect the predictive result.
ENERGY AND BUILDINGS
(2022)
Article
Construction & Building Technology
Yapin Yang, Ying Sun, Mingsi Chen, Yuekuan Zhou, Ran Wang, Zhengxuan Liu
Summary: This paper develops a BIM-based fire safety management system platform for construction sites, which includes four subsystems: remote monitoring, fire visualization, multi-stage fire alarm, and escape route optimization. Results show that this system can provide informative and on-time fire protection measures to construction sites.
Article
Energy & Fuels
Ying Sun, Karthik Panchabikesan, Mahmood Mastani Joybari, Dave Olsthoorn, Alain Moreau, Miguel Robichaud, Fariborz Haghighat
JOURNAL OF ENERGY STORAGE
(2018)
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)