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Covering buildings with vegetation systems has been a significant feature of architectural design towards sustainability in recent years. This study reports an investigation on the green wall thermal performance compared to the bare wall on the northern facade of a 2-story residential building in the humid climate of Rasht during summertime. For experimental measurements, temperature and humidity data loggers were used for real-time data collection. Thereafter, an existing building was modeled in EnergyPlus for validation purposes. According to the results, a decrease in temperature and relative humidity was seen in the case of the building with a green wall. It was found that the green wall could drop the indoor temperature by 9% and also decrease the relative humidity level by 32%. Besides, in order to predict the green wall performance in a short time interval, a deep artificial neural network was trained from the experimental data and a 15-day weather dataset was collected and fed into the deep learning model. Moreover, the ENVI-met software is utilized to evaluate the effect of the green wall on the surrounding air. Findings indicated that the temperature in front of the green wall is slightly lower than the part of the wall without the plant. The highest temperate reduction was 0.36 °C at 12 p.m., which is insignificant.
Abdollah Baghaei Daemei; Elham Shafiee; Amir Arash Chitgar; Somayeh Asadi. Investigating the thermal performance of green wall: Experimental analysis, deep learning model, and simulation studies in a humid climate. Building and Environment 2021, 205, 108201 .
AMA StyleAbdollah Baghaei Daemei, Elham Shafiee, Amir Arash Chitgar, Somayeh Asadi. Investigating the thermal performance of green wall: Experimental analysis, deep learning model, and simulation studies in a humid climate. Building and Environment. 2021; 205 ():108201.
Chicago/Turabian StyleAbdollah Baghaei Daemei; Elham Shafiee; Amir Arash Chitgar; Somayeh Asadi. 2021. "Investigating the thermal performance of green wall: Experimental analysis, deep learning model, and simulation studies in a humid climate." Building and Environment 205, no. : 108201.
The integration of electric vehicles (EVs) into the energy industry has introduced an unprecedented complexity into the energy supply system due to the uncertain nature of such vehicles. The uncertainty of arrival and departure times, as well as the state of charge (SOC) in the arrival and departure of the EVs, is a big challenge in the entry of such vehicles and their facilities into energy networks. These days, a novel approach known as the EVs smart parking lot (SPL) is widely studied in the energy industry looking to manage the charging and discharging electricity of EVs as well as energy supply issues. This study proposes an SPL equipped with heat and power sources, including renewable and non-renewable technologies such as wind turbines, locally installed generating facilities consisting of combined heat and power (CHP) plants, micro-turbines, and heat and power storage systems. The operator of the SPL, in addition to supplying its electricity for sale to the power market, can sell the heat generated by the CHP units locally to maximize its profit. In addition, the proposed model for the SPL can handle the uncertain nature of EV arrivals and departures and the associated SOC level. It can also manage wind-power output and gauge optimal power prices based on hybrid robust-stochastic programming, which is implemented in a case study to confirm its practicality and effectiveness. The analysis shows the effectiveness of the proposed hybrid robust-stochastic operation model in maximizing the profit of the SPL driver and managing the uncertainty level of the system parameters.
Morteza Nazari-Heris; Mohammad Amin Mirzaei; Somayeh Asadi; Behnam Mohammadi-Ivatloo; Kazem Zare; Houtan Jebelli. A hybrid robust-stochastic optimization framework for optimal energy management of electric vehicles parking lots. Sustainable Energy Technologies and Assessments 2021, 47, 101467 .
AMA StyleMorteza Nazari-Heris, Mohammad Amin Mirzaei, Somayeh Asadi, Behnam Mohammadi-Ivatloo, Kazem Zare, Houtan Jebelli. A hybrid robust-stochastic optimization framework for optimal energy management of electric vehicles parking lots. Sustainable Energy Technologies and Assessments. 2021; 47 ():101467.
Chicago/Turabian StyleMorteza Nazari-Heris; Mohammad Amin Mirzaei; Somayeh Asadi; Behnam Mohammadi-Ivatloo; Kazem Zare; Houtan Jebelli. 2021. "A hybrid robust-stochastic optimization framework for optimal energy management of electric vehicles parking lots." Sustainable Energy Technologies and Assessments 47, no. : 101467.
Because of high detection accuracy, deep learning algorithms have recently become the focus of increased attention for fault detection diagnostic (FDD) analysis of heat, ventilation, and air conditioning (HVAC) systems. Among all the machine learning algorithms in the field, deep recurrent neural networks (DRNNs) are being widely used since they are capable of learning the complex, uncertain, and temporal-dependent nature of the faults. However, embedding DRNN in FDD applications is still subject to two challenges: (I) a bespoke DRNN configuration, out of conceivably infinite DRNN architectures, is not explored on the task of FDD for HVAC systems; (II) Hyperparameter optimization, which is a computationally expensive task due to its empirical nature, is not investigated. In this respect, seven DRNNs configurations are introudecd and tuned that can automatically detect faults of different degrees under the faulty and normal conditions. Then, a comprehensive study of hyperparameters is conducted to optimize and compare all the proposed configurations based on their accuracy and training computational time. By searching through different hidden layers and layer sizes, optimization methods, model regularization, and batching, the ultimate DRNN model is selected out of more than 200 experiments. All the training configuration files are publicly available. Also, a comparison is made between the proposed DRNN model and two other advanced data-driven techniques, namely, random forest (RF) and gradient boosting (GB). The final DRNN model outperforms RF and GB regression by a significant margin.
Saman Taheri; Amirhossein Ahmadi; Behnam Mohammadi-Ivatloo; Somayeh Asadi. Fault detection diagnostic for HVAC systems via deep learning algorithms. Energy and Buildings 2021, 250, 111275 .
AMA StyleSaman Taheri, Amirhossein Ahmadi, Behnam Mohammadi-Ivatloo, Somayeh Asadi. Fault detection diagnostic for HVAC systems via deep learning algorithms. Energy and Buildings. 2021; 250 ():111275.
Chicago/Turabian StyleSaman Taheri; Amirhossein Ahmadi; Behnam Mohammadi-Ivatloo; Somayeh Asadi. 2021. "Fault detection diagnostic for HVAC systems via deep learning algorithms." Energy and Buildings 250, no. : 111275.
Undoubtedly, coronavirus (COVID-19) has caused one of the biggest challenges of all times. The ongoing COVID-19 pandemic has caused more than 150 million infected cases and one million deaths globally as of May 5, 2021. Understanding the sentiment of people expressed in their social media comments can help in monitoring, controlling, and ultimately eradicating the disease. This is a sensitive matter as the threat of infectious disease significantly affects the way people think and behave in various ways. In this study, we proposed a novel method based on the fusion of four deep learning and one classical supervised machine learning model for sentiment analysis of coronavirus-related tweets from eight countries. Also, we analyzed coronavirus-related searches using Google Trends to better understand the change in the sentiment pattern at different times and places. Our findings reveal that the coronavirus attracted the attention of people from different countries at different times in varying intensities. Also, the sentiment in their tweets is correlated to the news and events that occurred in their countries including the number of newly infected cases, number of recoveries and deaths. Moreover, common sentiment patterns can be observed in various countries during the spread of the virus. We believe that different social media platforms have great impact on raising people’s awareness about the importance of this disease as well as promoting preventive measures among people in the community.
Mohammad Ehsan Basiri; Shahla Nemati; Moloud Abdar; Somayeh Asadi; U. Rajendra Acharrya. A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowledge-Based Systems 2021, 228, 107242 .
AMA StyleMohammad Ehsan Basiri, Shahla Nemati, Moloud Abdar, Somayeh Asadi, U. Rajendra Acharrya. A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowledge-Based Systems. 2021; 228 ():107242.
Chicago/Turabian StyleMohammad Ehsan Basiri; Shahla Nemati; Moloud Abdar; Somayeh Asadi; U. Rajendra Acharrya. 2021. "A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets." Knowledge-Based Systems 228, no. : 107242.
Fiber-reinforced polymer (FRP) wrapping is one of the current approaches for retrofitting projects. This study aims to compare the performance of carbon and glass fibers in increase of compressive strength and improvement of concrete seismic parameters. In this research, three classes of concrete compressive strength (20, 35, and 50 MPa) are considered. The samples are warped with 0, 1, 3, and 5 layers of both fibers and examined under stress–strain tests. The results show that by adding more FRP layers, an increase is seen in compressive strength and seismic parameters. However, their growth rates decline. Moreover, the effect of both types of fibers is greater on improvement of compressive strength and failure strain for higher strength concrete. In contrast, fibers are more influential on lower strength concrete regarding energy absorption and ductility. Carbon and glass fibers are more effective in enhancing of compressive strength and seismic parameters, respectively. Statistical analysis indicates that, by adding more FRP layers, a tangible improvement on compressive strength and seismic parameters of concrete can be observed. That is true, especially for lower strength concrete. Moreover, the results demonstrate that the effect of fibers on the mentioned quantities is generally more than the effect of concrete classes. Finally, the Lam & Teng model shows a good fit with the experimental stress–strain diagrams.
Seiyed Ali Haj Seiyed Taghia; Hamid Reza Darvishvand; Somayeh Asadi; Mohammad Pourhasan. A comparative study on the implementation of carbon and glass fibers wrappings to improve the compressive strength and seismic behavior of concrete. Asian Journal of Civil Engineering 2020, 21, 1 -10.
AMA StyleSeiyed Ali Haj Seiyed Taghia, Hamid Reza Darvishvand, Somayeh Asadi, Mohammad Pourhasan. A comparative study on the implementation of carbon and glass fibers wrappings to improve the compressive strength and seismic behavior of concrete. Asian Journal of Civil Engineering. 2020; 21 (8):1-10.
Chicago/Turabian StyleSeiyed Ali Haj Seiyed Taghia; Hamid Reza Darvishvand; Somayeh Asadi; Mohammad Pourhasan. 2020. "A comparative study on the implementation of carbon and glass fibers wrappings to improve the compressive strength and seismic behavior of concrete." Asian Journal of Civil Engineering 21, no. 8: 1-10.
The coordination of energy carriers in energy systems has significant benefits in enhancing the flexibility, efficiency, and sustainability characteristics of energy networks. These benefits are of great importance for multi-carrier energy networks due to the complexity of obtaining optimal dispatch, considering the non-convex nature of their energy conversion. The current study proposes a robust operation model for the coordination of multi-carrier systems, including electricity, gas, heat, and water carriers concerning thermal energy storage technology. Thermal energy storage is for storing extra heat generated by combined heat and power (CHP) plants and boilers in time intervals with low heat demand on the system and discharging it when required. Energy network operators should have the capability to manage uncertain energy loads to study the impact of load variation on the decision-making process in network operation. Accordingly, this study employs an information gap decision theory (IGDT) method to model the uncertainty of the power demand in optimal system operation. By applying the IGDT approach, the operator of the energy system can use the appropriate methodology to obtain a robust optimal operation. Such a modeling approach helps the operator to make suitable decisions about probable variations in power load. The introduced model is applied in a test system for evaluating the performance and effectiveness of the introduced scheme.
Morteza Nazari-Heris; Behnam Mohammadi-Ivatloo; Somayeh Asadi. Optimal Operation of Multi-Carrier Energy Networks Considering Uncertain Parameters and Thermal Energy Storage. Sustainability 2020, 12, 5158 .
AMA StyleMorteza Nazari-Heris, Behnam Mohammadi-Ivatloo, Somayeh Asadi. Optimal Operation of Multi-Carrier Energy Networks Considering Uncertain Parameters and Thermal Energy Storage. Sustainability. 2020; 12 (12):5158.
Chicago/Turabian StyleMorteza Nazari-Heris; Behnam Mohammadi-Ivatloo; Somayeh Asadi. 2020. "Optimal Operation of Multi-Carrier Energy Networks Considering Uncertain Parameters and Thermal Energy Storage." Sustainability 12, no. 12: 5158.
Multi-chiller systems with thermal storage units are widely used to supply the cooling load of residential and commercial buildings. Due to the significant energy consumption, economic dispatch has been a crucial issue in multi-chiller systems. This paper presents a robust optimization approach for the day-ahead scheduling of a multi-chiller system with a chilled-water storage unit. The objective function aims to minimize the overall electricity cost of the system under hourly electricity prices, considering the uncertainty associated with the cooling load. Two case studies with different cooling load profiles are considered in evaluating the effect of the storage unit in increasing the robustness of total cost against the changes in the uncertain parameter. The multi-chiller system with the storage unit is modeled as non-linear programming solved with the General Algebraic Modeling System (GAMS), and the optimal partial load ratio of chillers and charge/discharge status of the storage unit are determined. In both case studies, the performance of the system with/without the storage unit is analyzed. The results demonstrate that the integration of the chilled-water storage unit increases the robustness of total cost against the variation of cooling load, as well as reducing the overall electricity cost.
Milad Sadat-Mohammadi; Somayeh Asadi; Mahmoud Habibnezhad; Houtan Jebelli. Robust scheduling of multi-chiller system with chilled-water storage under hourly electricity pricing. Energy and Buildings 2020, 218, 110058 .
AMA StyleMilad Sadat-Mohammadi, Somayeh Asadi, Mahmoud Habibnezhad, Houtan Jebelli. Robust scheduling of multi-chiller system with chilled-water storage under hourly electricity pricing. Energy and Buildings. 2020; 218 ():110058.
Chicago/Turabian StyleMilad Sadat-Mohammadi; Somayeh Asadi; Mahmoud Habibnezhad; Houtan Jebelli. 2020. "Robust scheduling of multi-chiller system with chilled-water storage under hourly electricity pricing." Energy and Buildings 218, no. : 110058.
Falling from heights is the primary cause of death and injuries at construction sites. As loss of balance has a fundamental effect on falling, it is important to understand postural regulation behavior during construction tasks at heights, especially those that require precise focus in an upright standing position (therefore, a dual-task demand on focus). Previous studies examined body sway during a quiet stance and dual tasks to understand latent factors affecting postural balance. Despite the success of these studies in discovering underlying factors, they lack a comprehensive analysis of a task's simultaneous cognitive load, postural sway, and visual depth. To address this limitation, this paper aims to examine construction workers' postural stability and task performance during the execution of visual construction tasks while standing upright on elevated platforms. To that end, two non-intrusive neurophysiological tests, a hand-steadiness task (HST) and a pursuit task (PT), were developed for construction tasks in a virtual environment (VE) as performance-based means to assess the cognitive function of workers at height. Workers' postural stability was measured by recording the mapped position of the Center of Pressure (COP) of the body on a posturography force plate, and the postural sway metrics subsequently calculated. A laboratory experiment was designed to collect postural and task performance data from 18 subjects performing the two batteries of tests in the virtual environment. The results demonstrated a significant decrease in the Root-Mean Square (RMS) of COP along the anterior-posterior axis during the Randomized Pursuit Task (RPT) and maximum body sway of the center of pressure (COP) in the mediolateral direction during both tests. Also, subjects exposed to high elevation predominately exhibit higher accuracy for RPT (P-value = 0.02) and lower accuracy for HST (P-value = 0.05). The results show that the combination of elevation-related visual depth and low-complexity dual tasks impairs task performance due to the elevation-induced visual perturbations and anxiety-driven motor responses. On the other hand, in the absence of visual depth at height, high task complexity surprisingly improves the pursuit tracking performance. As expected, during both tasks, alterations in postural control were manifested in the form of a body sway decrement as a compensatory postural strategy for accomplishing tasks at high elevation.
Mahmoud Habibnezhad; Jay Puckett; Houtan Jebelli; Ali Karji; Mohammad Sadra Fardhosseini; Somayeh Asadi. Neurophysiological testing for assessing construction workers' task performance at virtual height. Automation in Construction 2020, 113, 103143 .
AMA StyleMahmoud Habibnezhad, Jay Puckett, Houtan Jebelli, Ali Karji, Mohammad Sadra Fardhosseini, Somayeh Asadi. Neurophysiological testing for assessing construction workers' task performance at virtual height. Automation in Construction. 2020; 113 ():103143.
Chicago/Turabian StyleMahmoud Habibnezhad; Jay Puckett; Houtan Jebelli; Ali Karji; Mohammad Sadra Fardhosseini; Somayeh Asadi. 2020. "Neurophysiological testing for assessing construction workers' task performance at virtual height." Automation in Construction 113, no. : 103143.
Alaska is at the forefront of climate change and subject to salient challenges including energy consumption. It is important to understand Alaskans' perceptions and opinions about energy consumption to solve Alaska's domestic energy problems and creating a sustainable future. However, it is challenging to collect public opinions about energy consumption using conventional survey methods, which are often expensive, labor-intensive, and slow. This study utilizes information-rich Twitter data to investigate Alaskans' perceptions and opinions on various energy sources and in particular clean energy sources. Using the geotagged Twitter data collected in Alaska from 2014 to 2016, a lexicon-based sentiment analysis approach was first applied to analyze the polarity in the expressed opinions. Further, a novel fuzzy-based theory is employed to derive the sentiment of the opinion in each tweet. The results indicate that there is a valuable growth rate for a set of energy-related keywords, such as “sun”, “power”, and “nuclear”. The rank of top 20 renewable energy-related keywords shows the word “Tidal” has the highest ranking followed by “solar panel”. Moreover, the attention to various types of energy is increasing dramatically among Alaskans. Importantly, Alaskans' attitudes toward energy and renewable energy changed positively from 2014 to 2016, indicating that Alaskans' energy choices are more acceptive towards or even favor renewable energy in the future.
Moloud Abdar; Mohammad Ehsan Basiri; Junjun Yin; Mahmoud Habibnezhad; Guangqing Chi; Shahla Nemati; Somayeh Asadi. Energy choices in Alaska: Mining people's perception and attitudes from geotagged tweets. Renewable and Sustainable Energy Reviews 2020, 124, 109781 .
AMA StyleMoloud Abdar, Mohammad Ehsan Basiri, Junjun Yin, Mahmoud Habibnezhad, Guangqing Chi, Shahla Nemati, Somayeh Asadi. Energy choices in Alaska: Mining people's perception and attitudes from geotagged tweets. Renewable and Sustainable Energy Reviews. 2020; 124 ():109781.
Chicago/Turabian StyleMoloud Abdar; Mohammad Ehsan Basiri; Junjun Yin; Mahmoud Habibnezhad; Guangqing Chi; Shahla Nemati; Somayeh Asadi. 2020. "Energy choices in Alaska: Mining people's perception and attitudes from geotagged tweets." Renewable and Sustainable Energy Reviews 124, no. : 109781.
Though Building Information Modeling (BIM) has been proposed as a lean solution for the construction industry, its implementation would itself benefit from a proactive lean approach. This paper aims to study the implementation of BIM in Facilities Management (FM), and explores the synergistic potential of a lean approach. This was carried out through an integrative review of existing literature. BIM-FM implementation was categorized into three phases, which were analyzed to uncover the challenges and barriers faced in each; and explore the potential of a proactive lean approach to counter them. A number of key findings emerged. The existence of inefficiencies and variability in information management leading to an increase in labor hours was identified as a persistent problem in BIM-FM implementation. This had been derived by systematically mapping the challenges to their resultant effects on business processes based on the seven identified wastes in business. The paper provides both academics and practitioners with a collated list of issues based on a new way of examining BIM in FM implementation. It discusses the need for and synergistic potential of lean concepts to reduce information and time waste.
Saratu Terreno; Somayeh Asadi; Chimay Anumba. An Exploration of Synergies between Lean Concepts and BIM in FM: A Review and Directions for Future Research. Buildings 2019, 9, 147 .
AMA StyleSaratu Terreno, Somayeh Asadi, Chimay Anumba. An Exploration of Synergies between Lean Concepts and BIM in FM: A Review and Directions for Future Research. Buildings. 2019; 9 (6):147.
Chicago/Turabian StyleSaratu Terreno; Somayeh Asadi; Chimay Anumba. 2019. "An Exploration of Synergies between Lean Concepts and BIM in FM: A Review and Directions for Future Research." Buildings 9, no. 6: 147.