Article
Energy & Fuels
Leilei Shi, Junhui Gong, Chunjie Zhai
Summary: A hybrid optimization algorithm combining PSO and GA showed improved performance in determining the pyrolysis kinetics of biomass, achieving higher accuracy and population diversity in the process.
Article
Construction & Building Technology
Yingjun Ruan, Jiacheng Ma, Hua Meng, Fanyue Qian, Tingting Xu, Jiawei Yao
Summary: Demand response is an effective method for achieving energy flexibility, and preheating is an important means of storing energy and reducing heat load. The energy flexibility of a building depends on the types of DR events, the thermal performance of the building envelope, and weather conditions. This study evaluated the energy flexibility of a residential building in Kitakyushu, Japan using a simulation-based method, and found that high insulation buildings can improve energy efficiency and load reduction at specific temperatures. Additionally, key indicators for energy reduction were identified.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Construction & Building Technology
Mirzhan Kaderzhanov, Shazim Ali Memon, Assemgul Saurbayeva, Jong R. Kim
Summary: This research aims to analyze the energy consumption of residential buildings in Kazakhstan and reduce energy consumption by optimizing the envelope configuration. The results show that optimized measures can significantly reduce energy consumption during heating and cooling periods.
Article
Construction & Building Technology
Man Wang, Borong Lin
Summary: This study proposes two data-driven control strategies for room temperature control in buildings. XGBoost and LSTM are used for energy consumption and room temperature prediction. One strategy predicts parameters for on-off states, while the other predicts parameters for power-on states. Results show that the second strategy successfully trains a usable DDPG model for controlling building HVAC systems.
BUILDING AND ENVIRONMENT
(2023)
Article
Computer Science, Artificial Intelligence
Junhao Huang, Bing Xue, Yanan Sun, Mengjie Zhang, Gary G. Yen
Summary: Neural architecture search (NAS) is a popular research topic in deep learning community due to its potential in automating the construction of deep models. Among various NAS approaches, evolutionary computation (EC) stands out for its capability of gradient-free search. However, most current EC-based NAS approaches have the limitation of discrete evolution, making it difficult to handle the number of filters for each layer flexibly. Additionally, EC-based NAS methods are criticized for their inefficiency in performance evaluation, often requiring full training of hundreds of candidate architectures. This work proposes a split-level particle swarm optimization (PSO) approach to address these issues and achieves superior performance on image classification benchmarks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jia-Wei Zhang, Fang Liu, Huo-Nian Tu, Enrique Herrera-Viedma
Summary: A sequential modelling approach is proposed in this paper to address the complexity in decision making. The missing values can be estimated using particle swarm optimization based on the sequential model, and a granularity-based method is introduced to improve additive consistency. The study reveals the existence of multiple optimal solutions in decision making problems.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Xiangyu Zhang, Yue Chen, Andrey Bernstein, Rohit Chintala, Peter Graf, Xin Jin, David Biagioni
Summary: This paper develops an intelligent grid-interactive building controller using reinforcement learning and a global-local policy search method. The proposed method outperforms existing approaches and approaches the performance of an oracle model in testing scenarios.
IEEE TRANSACTIONS ON SMART GRID
(2022)
Article
Computer Science, Information Systems
Daniel Leal Souza, Rodrigo Lisboa Pereira, Mario T. R. Serra Neto, Marco A. F. Mollinetti, Otavio Noura Teixeira, Roberto C. L. De Oliveira
Summary: The Multi-Swarm approach allows for the use of different configurations between two or more populations of particles to improve the optimization process. This article proposes a local/global stochastic interconnection method for the Multi-Swarm algorithm and introduces a local search method for refining previously obtained solutions. The performance of these approaches is evaluated using ten constrained engineering design optimization problems and compared to existing solutions in the scientific literature, showing significant improvements.
Article
Thermodynamics
Wai Lam Ng, Min Yee Chin, Jinqin Zhou, Kok Sin Woon, Ann Ying Ching
Summary: A green building certification system is crucial for sustainable city development, but the importance of embodied energy and thermal insulation has often been overlooked. This study evaluates the energy consumption and insulation performance of non-green and green-rated non-residential buildings, and finds that insulated building envelopes can significantly reduce cooling demands.
Article
Construction & Building Technology
Giorgio Baldinelli, Jacek A. Schnotale, Francesco Bianchi, Agnieszka A. Lechowska, Andrea Presciutti
Summary: A new wall with variable thermophysical properties is introduced, which uses pipes on the inner and outer surfaces to transport heat in the thickness direction. Numerical and dynamic simulations show that the system performs well, with quick response to external conditions. It performs best in moderate climates, reducing energy losses and gains by more than 50%, but shows less effectiveness in cold climates.
ENERGY AND BUILDINGS
(2022)
Article
Construction & Building Technology
Dalia Tarek, M. M. Ahmed, Hesham Sameh Hussein, Abdullah M. Zeyad, Abdullah M. Al-Enizi, Ayman Yousef, Ayman Ragab
Summary: This study explored the potential of producing geopolymer bricks from industrial waste and compared their physico-mechanical properties with conventional bricks. The feasibility of using the geopolymer bricks in social residential buildings in Egypt was examined, and the energy-saving and carbon dioxide emissions reduction effects were simulated using software. The financial viability of the geopolymer bricks was also analyzed. The results show that the geopolymer bricks are a significant improvement in environmental sustainability.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2022)
Article
Computer Science, Artificial Intelligence
Rui Wang, Kuangrong Hao, Biao Huang, Xiuli Zhu
Summary: An adaptive particle swarm optimization algorithm (SR-PSO-MAES) based on speciation, species regulation, and local search is proposed to solve multimodal optimization problems (MMOPs). The experimental results validate the superiority of the proposed algorithm compared to 15 other algorithms.
APPLIED SOFT COMPUTING
(2023)
Article
Construction & Building Technology
Ugur Acar, Onder Kaska, Nehir Tokgoz
Summary: This study focused on multi-objective optimization of building envelope parameters to enhance the energy and economic performance of buildings, with experiments conducted in two provinces of Turkey showing that correct selection of parameters at the preliminary design stage can significantly reduce life cycle costs and provide better solutions for zero energy buildings.
JOURNAL OF BUILDING ENGINEERING
(2021)
Article
Construction & Building Technology
YiQin Xu, Yulia T. Peet
Summary: This study investigates the impact of intermittent operation of AC equipment on indoor temperature distribution, air flow, and cycle variability using Computational Fluid Dynamics. It found that heat transfer from walls and turbulent intermittency of indoor air affect the duration of cooling and heating cycles, leading to consistent overcooling in central AC systems controlled by a single thermostat placed in the hallway. These findings are important for electric grid management and HVAC system design and control improvement.
ENERGY AND BUILDINGS
(2021)
Article
Construction & Building Technology
Giuseppe Aruta, Fabrizio Ascione, Nicola Bianco, Gerardo Maria Mauro, Giuseppe Peter Vanoli
Summary: This study explores the use of machine learning techniques to predict the energy performance of a zero energy building in Benevento, Southern Italy. A simulation and optimization framework is used, with artificial neural networks and a genetic algorithm, to minimize heating energy costs and ensure thermal comfort. The results show that optimized control is crucial for sustainable building design, and the proposed framework can be implemented in real-time for model predictive control.
ENERGY AND BUILDINGS
(2023)
Article
Engineering, Civil
Youssef Bichiou, Hesham A. Rakha
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2019)
Article
Acoustics
Y. Bichiou, A. Abdelkefi, M. R. Hajj
JOURNAL OF VIBRATION AND CONTROL
(2016)
Article
Acoustics
Y. Bichiou, A. O. Nuhait, A. Abdelkefi, M. R. Hajj
JOURNAL OF VIBRATION AND CONTROL
(2016)
Article
Engineering, Civil
Amara Loulizi, Youssef Bichiou, Hesham Rakha
JOURNAL OF ADVANCED TRANSPORTATION
(2019)
Article
Green & Sustainable Science & Technology
Amara Loulizi, Youssef Bichiou, Hesham Rakha
Article
Energy & Fuels
Mohammad Dabbagh, Moncef Krarti
Summary: This study evaluates the potential energy and peak demand savings of optimal controls of switchable transparent insulation systems applied to smart windows in US residential buildings. It finds that optimized controls can significantly reduce energy consumption, especially in hot climates, and have important energy saving potential when applied to residential buildings in the US.
Article
Energy & Fuels
Remy Carlier, Mohammad Dabbagh, Moncef Krarti
Summary: This paper assesses the energy benefits of switchable insulation systems (SIS) when used as shades for windows and dynamic insulation for exterior walls in residential buildings in European countries. The results show that SIS-integrated windows can achieve significant energy savings, including elimination of mechanical cooling and up to 44% reduction in heating energy use for both dwelling types in Belgium. Furthermore, deploying SIS to both windows and exterior walls can provide even greater energy efficiency and thermal comfort benefits, especially in milder climates like Belgium and Spain.
Article
Energy & Fuels
Archan Shah, Moncef Krarti, Joe Huang
Summary: This paper evaluates the energy performance of shallow ground source heat pumps and presents a systematic approach to model their energy efficiency benefits. The study finds that shallow ground source heat pumps are more energy efficient than conventional air-to-air heat pumps in most climate zones in California, with specific configurations being cost-effective in certain areas.
Article
Energy & Fuels
Emily K. Schwartz, Moncef Krarti
Summary: This paper reviews the adoption status of energy efficiency and renewable energy technologies in US residential buildings, and analyzes the main factors influencing their adoption rates. Technologies with high adoption rates are driven by code and standard requirements, incentives through green certifications, low implementation costs, and public acceptance. In contrast, technologies with low adoption rates have longer payback periods, limited incentives or promotion, or are incompatible with existing systems.
Review
Energy & Fuels
Abdurahman Alrobaie, Moncef Krarti
Summary: This paper provides an extensive review of data-driven methods for building energy consumption prediction and compares the advantages, limitations, and applications of various approaches. It also summarizes feature engineering methods and commonly used building feature selection and processing techniques. The review highlights the gap between existing frameworks and recent case studies, emphasizing the need for a flexible M&V analysis framework.
Article
Green & Sustainable Science & Technology
Moncef Krarti, Mohammad Aldubyan
Summary: This paper presents a new optimization-based analysis framework for evaluating the cost benefits of retrofit programs for existing housing stocks in Saudi Arabia, with the aim of reducing their electrical peak demands. The study found that optimal retrofits are more effective in reducing peak demand and annual energy consumption for housing stocks in Saudi Arabia compared to the deployment of rooftop PV systems.
Correction
Green & Sustainable Science & Technology
Moncef Krarti, Mohammad Aldubyan
Proceedings Paper
Transportation Science & Technology
Kyungwon Kang, Youssef Bichiou, Hesham A. Rakha, Ahmed Elbery, Hao Yang
2019 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC)
(2019)
Proceedings Paper
Automation & Control Systems
Kyungwon Kang, Ahmed Elbery, Hesham A. Rakha, Youssef Bichiou, Hao Yang
2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC)
(2018)
Article
Green & Sustainable Science & Technology
Amara Loulizi, Hesham Rakha, Youssef Bichiou
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
(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)
