Article
Construction & Building Technology
Mohammad Pazouki, Kamran Rezaie, Ali Bozorgi-Amiri
Summary: This study utilizes mathematical modeling to determine the best strategy for building energy retrofit, considering economic and environmental criteria and using a generalized mathematical model to optimize energy efficiency. The results indicate that implementing the obtained strategy can significantly affect buildings' energy saving.
ENERGY AND BUILDINGS
(2021)
Article
Green & Sustainable Science & Technology
Aron Powers, Messiha Saad
Summary: Understanding energy usage and potential savings in existing buildings is crucial for cost reduction, environmental impact, and legal compliance. This case study analyzed the lighting systems in Washington State University Tri-Cities' Floyd & East buildings and found that retrofitting fluorescent lights with LEDs and occupancy sensors can achieve significant energy savings. The study also demonstrated a break-even point within 15 months of operation.
Article
Environmental Sciences
Jonathan Romero-Cuellar, Cristhian J. Gastulo-Tapia, Mario R. Hernandez-Lopez, Cristina Prieto Sierra, Felix Frances
Summary: This research develops a new post-processing method called GMCP, which combines clustering and Gaussian mixture models to manage heteroscedastic errors in monthly streamflow predictions. The results show that GMCP outperforms traditional methods in generating reliable and accurate predictions, especially in dry catchments. GMCP is a promising solution for monthly hydrological prediction and water resources management.
Article
Construction & Building Technology
Vicente Gutierrez Gonzalez, German Ramos Ruiz, Carlos Fernandez Bandera
Summary: Calibrating building energy models is crucial for applications like demand response and model predictive control. This research examines different methods for characterizing thermal interaction with the ground in BEMs, comparing conventional and new approaches. By analyzing real building data, the study identifies EnergyPlus components that offer cost-effective characterization of ground-slab interaction.
ENERGY AND BUILDINGS
(2022)
Article
Green & Sustainable Science & Technology
Muhammed Yildirim, Hasan Polat
Summary: The built environment plays a significant role in global energy consumption and carbon emissions, and retrofitting existing buildings using BIM-based energy modeling can effectively improve energy efficiency. This research study investigated the potential of BIM in evaluating the effectiveness of refurbishment scenarios on a residential building. The results showed that the optimum alternative scenario reduced fuel and electricity consumption by 61% and 64% respectively, with a payback period of 12 years.
Review
Green & Sustainable Science & Technology
Xiu'e Yang, Shuli Liu, Yuliang Zou, Wenjie Ji, Qunli Zhang, Abdullahi Ahmed, Xiaojing Han, Yongliang Shen, Shaoliang Zhang
Summary: This paper provides up-to-date approaches for predicting energy-saving effects in large-scale building retrofit, including data-driven, physics-based, and hybrid approaches. It highlights several key issues in current prediction models, such as ignoring performance differences, prebound and rebound effects, and occupant willingness to retrofit. The study is of great importance in promoting the development of energy-saving potential prediction models and formulating appropriate retrofit strategies for large-scale buildings.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Construction & Building Technology
Mengjie Han, Zhenwu Wang, Xingxing Zhang
Summary: In recent years, uncertainties in building energy performance have increased, posing challenges in data collection and establishing data pipelines at an urban scale. However, the use of Gaussian mixture models and Expectation-Maximization algorithm to generate synthetic data points has shown consistent representation with real data.
Article
Computer Science, Artificial Intelligence
Jiyang Xie, Zhanyu Ma, Jing-Hao Xue, Guoqiang Zhang, Jian Sun, Yinhe Zheng, Jun Guo
Summary: This paper introduces a DS-UI framework that combines DNN classifier with MoGMM to enhance Bayesian estimation-based UI in image recognition. The DS-UI improves image recognition accuracy by directly calculating probabilities, and proposes a dual-supervised stochastic gradient-based variational Bayes algorithm for optimization.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Environmental Sciences
Amir H. Kohanpur, Siddharth Saksena, Sayan Dey, J. Michael Johnson, M. Sadegh Riasi, Lilit Yeghiazarian, Alexandre M. Tartakovsky
Summary: Estimating uncertainty in flood model predictions is crucial for various applications. This study focuses on uncertainty in physics-based urban flooding models, considering model complexity, uncertainty in input parameters, and the effects of rainfall intensity. The ICPR model is used to quantify floodwater depth prediction uncertainty, with results showing localized uncertainties. Model simplifications lead to overconfident predictions, while increasing model resolution reduces uncertainty but increases computational cost. The multilevel MC method is employed to reduce cost when estimating uncertainty in a high-resolution ICPR model. Utilizing ensemble estimates, the proposed framework improves flood depth forecasting accuracy compared to the ICPR model's mean prediction, even with limited measurements.
WATER RESOURCES RESEARCH
(2023)
Article
Construction & Building Technology
Cindy Regnier, Paul Mathew, Jordan Shackelford, Sang Hoon Lee, Alastair Robinson, Travis Walter
Summary: Utility incentive programs are crucial for promoting energy efficiency in buildings, but they have been limited to single-component strategies. However, there is now a growing interest in developing multi-component system retrofits to achieve deeper energy savings. This paper presents the energy savings, demand reductions, and cost-effectiveness of 16 systems retrofit packages in the US, which are being used by utilities to inform incentive programs. The results show that packages with proven lighting and HVAC measures can provide significant energy and cost savings, and the choice of baseline for calculating savings has a substantial impact on program viability.
ENERGY AND BUILDINGS
(2022)
Article
Chemistry, Analytical
Qiuhui Xu, Shenfang Yuan, Tianxiang Huang
Summary: This paper investigates the use of Gaussian mixture models (GMM) for crack monitoring, proposing a multi-dimensional uniform initialization GMM method. By integrating multi-channel GW features to increase accuracy and stability in crack quantification, the research addresses the challenges of crack sensitivity in GW features and uncertainties in crack initiation and growth.
Article
Engineering, Multidisciplinary
Jacques H. Mclean, Matthew R. Jones, Brandon J. O'Connell, Eoghan Maguire, Tim J. Rogers
Summary: A wind turbine's power curve is vital for structural health monitoring, but existing probabilistic models often lack physical plausibility. This paper investigates two bounded Gaussian processes and demonstrates that a well-designed bounded model offers improved predictive uncertainty and accuracy compared to an unbounded model.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Chemistry, Multidisciplinary
Roeland De Meulenaere, Diederik Coppitters, Ale Sikkema, Tim Maertens, Julien Blondeau
Summary: This paper assesses the future thermodynamics performance of a retrofitted heat and power production unit using sparse polynomial chaos expansion (SPCE) method. It quantifies the main drivers and overall uncertainty, finding that the composition of the fuel and its heating value are the most impactful input parameters. Key furnace parameters exhibit skewed probability distribution and significant uncertainties.
APPLIED SCIENCES-BASEL
(2023)
Article
Energy & Fuels
Xinbin Liang, Zhuoxuan Liu, Jie Wang, Xinqiao Jin, Zhimin Du
Summary: Artificial intelligence has become a key technology in building energy conservation, particularly in complex building energy systems modeling. However, existing deep learning models lack robustness under distribution shift scenarios, limiting their real-world application. To address this, a cluster-based dataset splitting method is proposed to simulate distribution shift scenarios, and uncertainty quantification methods are adopted to improve overall model robustness. Experimental results demonstrate that the deep ensemble (DE) model outperforms other methods in terms of precision and robustness, followed by Bayesian neural network (BNN). The research framework provides a solid foundation for the real-world application of deep learning models.
Article
Engineering, Multidisciplinary
Kshitiz Upadhyay, Dimitris G. Giovanis, Ahmed Alshareef, Andrew K. Knutsen, Curtis L. Johnson, Aaron Carass, Philip Bayly, Michael D. Shields, K. T. Ramesh
Summary: Computational models of the human head play a crucial role in predicting traumatic brain injury, but they are associated with uncertainty and variability. This study proposes a data-driven framework for uncertainty quantification of computational head models, which reduces computational cost while providing accurate approximations. The framework employs manifold learning techniques and surrogate models to quantify uncertainty and variability in the simulated strain fields. The results highlight significant spatial variation in model uncertainty and reveal differences in uncertainty among strain-based brain injury predictor variables.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
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)