Review
Green & Sustainable Science & Technology
Shuo Chen, Guomin Zhang, Xiaobo Xia, Yixing Chen, Sujeeva Setunge, Long Shi
Summary: This literature review summarized three main categories of occupant behaviors in buildings, emphasizing the importance of actual occupancy and interactions with buildings in determining energy consumption. Behavioral efficiency was highlighted as an efficient and economical method, but improvements are needed in categorizing and quantifying behavioral inputs. Window opening behavior and gender energy impacts were also discussed, highlighting the significance of understanding variations in personal and environmental controls. Overall, the results are important for identifying key factors and considerations for energy simulation and software development in the future.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Construction & Building Technology
Flavian Emmanuel Sapnken, Mohammad M. Hamed, Bozidar Soldo, Jean Gaston Tamba
Summary: This paper explores the feasibility of using Machine Learning (ML) to predict buildings energy demand at the design stage. The results show that deep neural network (DNN) is the most efficient ML model, which can explain 96% of the energy consumption in buildings.
ENERGY AND BUILDINGS
(2023)
Article
Computer Science, Information Systems
Aryuanto Soetedjo, Sotyohadi Sotyohadi
Summary: This paper presents an approach to integrating an occupancy model and a real-time monitoring system for real-time modeling, using embedded devices and IoT technology. Experimental results show that the method effectively monitors the model, with data stored in an IoT cloud server for further analysis, and supports real-time operation of embedded devices.
Article
Green & Sustainable Science & Technology
Aram Makivierikko, Henrik Siepelmeyer, Hossein Shahrokni, David Enarsson, Olga Kordas
Summary: Households can provide demand-side flexibility by shifting their energy-intensive activities to off-peak hours. This study investigated the impact of a smartphone app that provided users with social comparison feedback and encouraged them to participate in pause hours. The results showed that the app achieved substantial peak-load consumption reductions and long-term engagement.
JOURNAL OF CLEANER PRODUCTION
(2023)
Article
Energy & Fuels
Jean Rouleau, Louis Gosselin
Summary: The COVID-19 lockdown led to changes in home occupancy and work patterns, impacting building energy consumption. During the initial lockdown period, electricity and hot water consumption patterns changed with a slight overall increase.
Article
Construction & Building Technology
Shuqin Chen, Yinyan Lv, Zhichao Wang, Yuhang Ma, Yurui Huang, Yichao Wang, Yuxuan Cai, Zhiqin Rao
Summary: This study collects real-time occupancy data and develops a Monte Carlo-based model to simulate and compare the building heating and cooling load differences caused by fixed occupancy schedules and random occupancy time series. The results show that using random occupancy schedules can accurately predict and assess the building loads.
JOURNAL OF BUILDING ENGINEERING
(2023)
Article
Multidisciplinary Sciences
Babak Khavari, Alexandros Korkovelos, Andreas Sahlberg, Mark Howells, Francesco Fuso Nerini
Summary: This study proposes a method to translate high-resolution raster population data into vector-based population clusters, with unique characteristics such as population, electrification rate, and urban-rural classification. Results show that modeled national electrification rates are consistent with those reported by the World Bank, and the urban/rural classification has an accuracy of 88%.
Article
Construction & Building Technology
Daniel L. Villa
Summary: The study utilized Building Energy Modeling, meter data, and climate projections to estimate the impact of heat waves on energy consumption and electric peak load, providing valuable information for planners to quantify risks. The methodology applied linear regression models to analyze uncertainty and assist institutions in addressing rapid growth with resilience.
ENERGY AND BUILDINGS
(2021)
Article
Construction & Building Technology
Ko Nakajima, Yuya Takane, Shinya Fukuba, Kazuki Yamaguchi, Yukihiro Kikegawa
Summary: The relationship between electricity consumption and outdoor air temperature in the Tokyo Metropolitan Area was analyzed using data from 1290 substations. The study revealed temporal-spatial variations in the sensitivity of electricity consumption to temperature. The results have implications for the development of efficient energy management systems and improvement of urban climate and building energy models.
ENERGY AND BUILDINGS
(2022)
Article
Economics
Benard M. Wabukala, Nicholas Mukisa, Susan Watundu, Olvar Bergland, Nichodemus Rudaheranwa, Muyiwa S. Adaramola
Summary: Renewable energy sources (RES) in Uganda face challenges in terms of affordability and theft. A study conducted a probabilistic assessment to categorize households based on electricity affordability in urban and rural areas. The study proposed alternative billing schemes to enhance legal connection and consumption of electricity. Results showed higher theft losses in rural households and a significant difference in revenue collected between urban and rural areas due to the number of legally connected households and their consumption levels. The study also highlighted the high initial connection fee and the below-average affordability of electricity for both rural and urban households in Uganda.
Article
Construction & Building Technology
Karen A. Kellogg, Nicol La Cumbre-Gibbs
Summary: Buildings in the United States consume a large amount of electricity and energy, and contribute to a significant portion of carbon dioxide emissions. The adoption of building codes plays a role in reducing per capita electrical consumption, but it leads to an increase in per capita natural gas consumption at the national level. Factors such as energy prices, climate conditions, and population density also contribute to variations in energy consumption.
ENERGY AND BUILDINGS
(2023)
Article
Thermodynamics
Rui Tang, Shengwei Wang, Shaobo Sun
Summary: Occupant behavior plays a crucial role in building energy consumption. A new concept of technology-guided occupant behavior is proposed to coordinate occupant behavior with energy-efficient technologies for building energy controls. On-site tests in a Hong Kong campus building demonstrated that technology-guided occupant behavior could significantly reduce energy consumption of central air-conditioning systems.
BUILDING SIMULATION
(2021)
Article
Construction & Building Technology
Yuan Su, Ziyu Miao, Linwei Wang, Luyuan Wang
Summary: This study focuses on the energy consumption and carbon emissions of large irregular commercial buildings and proposes promotion strategies for holistic design based on the findings. The research reveals that the building's energy consumption did not meet standard requirements and the indoor environmental quality and service satisfaction scores were low.
ENERGY AND BUILDINGS
(2022)
Article
Energy & Fuels
Pinar Usta, Basak Zengin
Summary: With the increase in energy usage and demand in buildings, energy saving has become a significant study topic in the building industry. Minimizing energy losses and using energy more efficiently is necessary to decrease energy demand. By comparing the energy efficiency of Autoclaved Aerated Concrete (AAC) and Brick, the energy scenario for Izmir City, Turkey Climatic Conditions is discussed.
Article
Construction & Building Technology
Zixin Jiang, Zheng O'Neill, Bing Dong
Summary: With the use of advanced sensing technologies, occupancy-based control (OBC) has shown significant energy-saving potential by adjusting temperature and ventilation settings based on real-time occupancy information. A two-year field testing in a medium-sized office building demonstrated a reduction of heating load by 4% to 11.2% and cooling load by 3.6% to 6.5% in the open space, along with a decrease of 8% to 16.3% in HVAC energy during extreme weather conditions. However, drawbacks such as thermal comfort issues and limitations in free cooling were also identified.
ENERGY AND BUILDINGS
(2023)
Article
Operations Research & Management Science
Duc-Hoc Tran, Jui-Sheng Chou, Duc-Long Luong
Summary: This work presents a new hybrid evolutionary approach, called the fuzzy clustering artificial bee colony approach (FABC), to optimize resource assignment and scheduling for non-unit repetitive projects (NRP). The proposed method outperforms benchmark algorithms in terms of project duration and optimal solution deviation.
OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Interdisciplinary Applications
Jui-Sheng Chou, Jeffisa Delaosia Kosasih, Wai K. Chong
Summary: The study developed a cloud evolutionary machine learning system aimed at providing user-friendly web analytics for solving engineering problems. The system successfully addressed classification and regression problems with high accuracy and improved performance metrics.
ENGINEERING WITH COMPUTERS
(2022)
Article
Energy & Fuels
Jui-Sheng Chou, Sheng-Ming Hsu
Summary: This study established an automated platform using machine learning and optimization methods to predict residential electricity demand in each city in Taiwan, generating an accurate and effective model. By providing monthly electricity consumption information through a web-based system, it helps power providers and government discuss policies and set energy use goals.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Construction & Building Technology
Jui-Sheng Chou, Dinh-Nhat Truong
Summary: The MOFBI algorithm utilizes chaotic maps and elite populations to explore and exploit multi-objective search spaces, providing more accurate approximations of Pareto-optimal solutions compared to other algorithms.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Computer Science, Artificial Intelligence
Dang-Trinh Nguyen, Jui-Sheng Chou, Duc-Hoc Tran
Summary: Project managers face challenges in balancing various resource factors, and this study proposes a framework that integrates BIM, MOO, and MCDM to find the best resource scheduling solution through modeling, multi-objective optimization, and multi-criteria decision analysis.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Construction & Building Technology
Jui-Sheng Chou, Misael Algape Karundeng, Dinh-Nhat Truong, Min-Yuan Cheng
Summary: This study proposes a method that combines deep learning and bio-inspired optimization to measure the deflection of reinforced concrete beams. The hybrid model achieves higher accuracy than traditional models and provides insights in similar visual surveillance tasks.
STRUCTURAL CONTROL & HEALTH MONITORING
(2022)
Article
Environmental Studies
Jui-Sheng Chou, Dillon-Brandon Fleshman, Dinh-Nhat Truong
Summary: This research reviews machine learning techniques for predicting house prices and compares their performance using four popular artificial intelligence methods. The study finds that the proposed particle swarm optimization-Bagging-ANNs hybrid model outperforms others in predicting house prices. The availability of multiple prediction models allows users to choose the most suitable one based on their needs and understanding of machine learning.
JOURNAL OF HOUSING AND THE BUILT ENVIRONMENT
(2022)
Article
Construction & Building Technology
Jui-Sheng Chou, Ngoc-Mai Nguyen
Summary: This paper proposes a novel artificial intelligence-based approach, called MOSS, for estimating scour depth at bridge piers. MOSS combines a metaheuristic optimization algorithm with efficient machine learning models to achieve the most effective system. The efficiency of MOSS is verified through three case studies and is found to outperform other approaches. The results show that MOSS is the most reliable and accurate method for predicting scour depth.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Mathematical & Computational Biology
Jui-Sheng Chou, Stela Tjandrakusuma, Chi-Yun Liu
Summary: This study uses deep learning models to predict the compressive strength of concrete. By comparing computer vision and conventional numerical data methods, it is found that computer vision methods outperform the traditional methods in terms of accuracy and reliability. The computer vision models were further optimized using a bio-inspired metaheuristic algorithm, resulting in the best prediction models.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
Article
Construction & Building Technology
Chi-Yun Liu, Jui-Sheng Chou
Summary: Bridge collapses and fractures have occurred due to lack of inspection and maintenance, and traditional visual inspection methods have limitations. This study presents an unmanned aerial vehicle (UAV) equipped with a Bayesian-optimized deep learning model for computer vision-based identification of deterioration patterns and segmented areas in composite bridge decks. The proposed module improves inspection accuracy, reduces labor safety hazards, and can be embedded in an artificial intelligence chip for consumer-grade UAVs dedicated to external bridge inspections.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Computer Science, Interdisciplinary Applications
Jui-Sheng Chou, Li-Ying Chen, Chi-Yun Liu
Summary: A baseline model for predicting the compressive strength of concrete was constructed using machine-learning methods. The model was optimized using a newly developed algorithm and historical data from concrete plants. An expert system was developed to facilitate quality management and improve the safety of concrete structures.
JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING
(2023)
Article
Engineering, Civil
Jui-Sheng Chou, Yu-Hsuan Chen, Chi-Yun Liu, Wai Oswald Chong
Summary: Due to the COVID-19 pandemic, construction sites in Taiwan have implemented measures such as limiting the number of workers and conducting independent work to prevent the spread of the virus. Unclear job handover and the presence of infected workers pose challenges to construction quality. Neglecting inspection processes can result in errors and poor quality. To address these issues, a literature analysis and in-depth interviews with subcontractors were conducted to identify quality management problems in private housing projects. Improvement measures and a simplified checklist were formulated based on practical feedback. An inspection application was also developed to facilitate construction practice, especially during the pandemic.
JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT
(2023)
Article
Energy & Fuels
Jui-Sheng Chou, Ngoc-Quang Nguyen
Summary: The energy sector needs to find a delicate balance between energy supply and demand. Accurate energy consumption forecasts can assist plant operators in achieving this goal. This study explores the application of various techniques from three categories of artificial intelligence, namely convolutional neural networks (CNNs), machine learning (ML), and time-series deep learning (DL), to predict short-term regional energy consumption.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2023)
Article
Computer Science, Interdisciplinary Applications
Dinh-Nhat Truong, Jui-Sheng Chou
Summary: This study develops a novel fuzzy adaptive forensic-based investigation algorithm (FAFBI) to optimize frequency-constrained structural dome design. The results show that FAFBI outperforms other compared algorithms in finding optimal values. The proposed FAFBI algorithm is an effective tool for solving mathematical optimization problems and for use in the design phase of structural construction.
MATHEMATICS AND COMPUTERS IN SIMULATION
(2023)
Article
Construction & Building Technology
Jui-Sheng Chou, Yu-Hsiu Chang, Asmare Molla, Wai Oswald Chong
Summary: Urban renewal involves different stakeholders with different expectations, such as residents and developers. This study analyzed completed and ongoing urban renewal projects and found that land integration is a crucial factor for success. Developers need to consider factors such as resident approval rates, complexity of ownership, environmental friendliness, high rewards, case review duration, and providing a vision of the community after renewal. Urban renewal also relies on government assistance and cooperation among developers, government, and landowners.
SUSTAINABLE CITIES AND SOCIETY
(2023)