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Prof. Geun Youn Yun
Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Korea

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0 Architectural Lighting
0 Renewable Energy
0 deep neural network
0 Occupant behavior and comfort
0 Building energy and performance

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Building energy and performance
deep neural network
Renewable Energy
Occupant behavior and comfort

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Journal article
Published: 05 July 2021 in Sustainability
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Urban heat islands (UHI) are a widely documented phenomenon that adversely increases urban overheating and, among other effects, contributes to heat-related mortalities and morbidities in urban areas. Consequently, comprehensive UHI-mitigating measures are essential for improving urban microclimate environments and contributing to salutogenic urban design practices. This study proposed urban cooling strategies involving different tree percentages and leaf area densities in a dense urban area during the summertime in Korea. The cooling effects of sixteen various combinations of proposed scenarios based on common urban tree types were studied via in-situ field measurements and numerical modeling, considering both vegetated and exposed areas. It was observed that by changing the characteristics of the leaf area density (LAD) per plant of our vegetated base area—for instance, from 4% trees to 60% trees, from a low LAD to a high LAD—the daily average and daily maximum temperatures were reduced by approximately 3 °C and 5.23 °C, respectively. The obtained results demonstrate the usefulness of urban trees to mitigate urban heating, and they are particularly useful to urban designers and policymakers in their efforts to minimize UHI effects.

ACS Style

Atefeh Tamaskani Esfehankalateh; Jack Ngarambe; Geun Yun. Influence of Tree Canopy Coverage and Leaf Area Density on Urban Heat Island Mitigation. Sustainability 2021, 13, 7496 .

AMA Style

Atefeh Tamaskani Esfehankalateh, Jack Ngarambe, Geun Yun. Influence of Tree Canopy Coverage and Leaf Area Density on Urban Heat Island Mitigation. Sustainability. 2021; 13 (13):7496.

Chicago/Turabian Style

Atefeh Tamaskani Esfehankalateh; Jack Ngarambe; Geun Yun. 2021. "Influence of Tree Canopy Coverage and Leaf Area Density on Urban Heat Island Mitigation." Sustainability 13, no. 13: 7496.

Journal article
Published: 30 April 2021 in iScience
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Summary A primary contributor to urban overheating is the urban heat island (UHI) formed due to increased urbanization. The adverse effects of UHI on building energy use are substantial and well documented. However, such effects are typically demonstrated through numerical simulations which are susceptible to modeling uncertainties and lack of validation resulting in a pressing research gap. Here, for the first time, we conduct a large-scale assessment to demonstrate the devastating impact of UHI on building energy consumption using real building energy use data. We find empirical evidence correlating UHI with building energy use; changes in average UHI intensity of 0.5 K correspond to an increase in monthly cooling energy consumption in a range of 0.17 kWh/m2–1.84 kWh/m2. The study validates theoretical evidence on the impact of UHI on building energy and proposes a highly innovative methodology to assess the impact of overheating on the energy balance of cities.

ACS Style

Mi Aye Su; Jack Ngarambe; Mat Santamouris; Geun Young Yun. Empirical evidence on the impact of urban overheating on building cooling and heating energy consumption. iScience 2021, 24, 102495 .

AMA Style

Mi Aye Su, Jack Ngarambe, Mat Santamouris, Geun Young Yun. Empirical evidence on the impact of urban overheating on building cooling and heating energy consumption. iScience. 2021; 24 (5):102495.

Chicago/Turabian Style

Mi Aye Su; Jack Ngarambe; Mat Santamouris; Geun Young Yun. 2021. "Empirical evidence on the impact of urban overheating on building cooling and heating energy consumption." iScience 24, no. 5: 102495.

Journal article
Published: 26 April 2021 in Sustainability
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Spectral power distribution (SPD) is an essential element that has considerable implications on circadian energy and the perception of lit environments. The present study assessed the potential influences of SPD on energy consumption (i.e., considering circadian energy), visual comfort, work performance and mood. Two lighting conditions based on light-emitting diode (LED) and organic light-emitting diode (OLED) were used as proxies for SPDs of different spectral content: dominant peak wavelength of 455 nm (LED) and 618 nm (OLED). Using measured photometric values, the circadian light (CL), melatonin suppression (MS), and circadian efficacy (CE) of the two lighting sources were estimated via a circadian-phototransduction model and compared. Additionally, twenty-six participants were asked to evaluate the said lit environments subjectively in terms of visual comfort and self-reported work performance. Regarding circadian lighting and the associated energy implications, the LED light source induced higher biological actions with relatively less energy than the OLED light source. For visual comfort, OLED lighting-based conditions were preferred to LED lighting-based conditions, while the opposite was true when considering work performance and mood. The current study adds to the on-going debate regarding human-centric lighting, particularly considering the role of SPD in energy-efficient and circadian lighting practices.

ACS Style

Jack Ngarambe; Inhan Kim; Geun Yun. Influences of Spectral Power Distribution on Circadian Energy, Visual Comfort and Work Performance. Sustainability 2021, 13, 4852 .

AMA Style

Jack Ngarambe, Inhan Kim, Geun Yun. Influences of Spectral Power Distribution on Circadian Energy, Visual Comfort and Work Performance. Sustainability. 2021; 13 (9):4852.

Chicago/Turabian Style

Jack Ngarambe; Inhan Kim; Geun Yun. 2021. "Influences of Spectral Power Distribution on Circadian Energy, Visual Comfort and Work Performance." Sustainability 13, no. 9: 4852.

Journal article
Published: 22 April 2021 in Sustainable Cities and Society
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Prevailing weather conditions and urban morphology are among the fundamental drivers of the radiative and turbulent exchange processes that inherently result in an urban heat island (UHI). The influence of such drivers varies with temporal changes and must be estimated using recent and sufficiently large meteorological and land-use datasets. We used the most recent in-situ temperature observations collected from a spatially extensive network of 54 automatic weather stations (AWS) gathered over a period of nine years to quantify the influence of wind speed, cloud coverage and land use characteristics on the magnitude of UHI in Seoul city. Through an analysis of variance and combined clustering techniques, we found increased UHI intensities (UHII) under low wind speeds, clear skies and densely built areas. Changes of 0.5 m/s in wind speeds and 1 octa in cloud coverage corresponded to 0.20 °C and 0.24 °C reductions in UHII, respectively. Also, statistically significant differences in peak diurnal UHII values of 1.2 °C were estimated between densely built and sparsely built areas. The obtained results shed light on the factors likely to influence UHI development in Seoul city, and fuel scientific discourse on the development of sustainable cities and effective policies for the management of the built environment.

ACS Style

Jack Ngarambe; Jin Woo Oh; Mi Aye Su; Mat Santamouris; Geun Young Yun. Influences of wind speed, sky conditions, land use and land cover characteristics on the magnitude of the urban heat island in Seoul: An exploratory analysis. Sustainable Cities and Society 2021, 71, 102953 .

AMA Style

Jack Ngarambe, Jin Woo Oh, Mi Aye Su, Mat Santamouris, Geun Young Yun. Influences of wind speed, sky conditions, land use and land cover characteristics on the magnitude of the urban heat island in Seoul: An exploratory analysis. Sustainable Cities and Society. 2021; 71 ():102953.

Chicago/Turabian Style

Jack Ngarambe; Jin Woo Oh; Mi Aye Su; Mat Santamouris; Geun Young Yun. 2021. "Influences of wind speed, sky conditions, land use and land cover characteristics on the magnitude of the urban heat island in Seoul: An exploratory analysis." Sustainable Cities and Society 71, no. : 102953.

Research article
Published: 26 January 2021 in International Journal of Energy Research
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The use of photovoltaic (PV) systems has drawn attention as a solution to reduce the dependence on fossil fuel for building energy needs. Moreover, incorporating energy storage systems (ESSs) in PV systems can optimise electric energy costs by increasing dependency on PV‐generated energy during electric peak load times. However, current ESSs have limited capacities making it difficult to fully maximise PV‐generated energy. We propose a novel integrated energy‐efficient system for PV, ESS and electric heat pump (EHP) that maximises the usage of PV energy, optimises ESS usage and reduces EHP energy consumption costs. The components of the proposed integrated system are linked with a deep learning (DL)‐based algorithm that forecasts PV energy generation and energy demand of the EHP. The proposed system schedules the charging/discharging time of ESSs depending on peak load times, the forecasted EHP electric demand, and PV‐generated energy. The data used were collected for 10 months from a retail shop equipped with an EHP and ESS. We found that the developed DL‐based forecasting models for PV and EHP are accurate and reliable (ie, R2 above 0.95). Also, the results show that the proposed integrated energy‐efficient PV‐ESS‐EHP system saves 12% of the total annual electric costs, which corresponds to 1 285 291 Won. The proposed system ensures an efficient method to maximise PV‐generated energy resulting in reduced dependency on fossil fuels for building energy needs.

ACS Style

Patrick Nzivugira Duhirwe; Jun Kwon Hwang; Jack Ngarambe; Suhgoo Kim; Kyungjae Kim; Kwanwoo Song; Geun Young Yun. A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs. International Journal of Energy Research 2021, 45, 9306 -9325.

AMA Style

Patrick Nzivugira Duhirwe, Jun Kwon Hwang, Jack Ngarambe, Suhgoo Kim, Kyungjae Kim, Kwanwoo Song, Geun Young Yun. A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs. International Journal of Energy Research. 2021; 45 (6):9306-9325.

Chicago/Turabian Style

Patrick Nzivugira Duhirwe; Jun Kwon Hwang; Jack Ngarambe; Suhgoo Kim; Kyungjae Kim; Kwanwoo Song; Geun Young Yun. 2021. "A novel deep learning‐based integrated photovoltaic, energy storage system and electric heat pump system: Optimising energy usage and costs." International Journal of Energy Research 45, no. 6: 9306-9325.

Research article
Published: 07 December 2020 in PLOS ONE
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The effects of heat waves (HW) are more pronounced in urban areas than in rural areas due to the additive effect of the urban heat island (UHI) phenomenon. However, the synergies between UHI and HW are still an open scientific question and have only been quantified for a few metropolitan cities. In the current study, we explore the synergies between UHI and HW in Seoul city. We consider summertime data from two non-consecutive years (i.e., 2012 and 2016) and ten automatic weather stations. Our results show that UHI is more intense during HW periods than non-heat wave (NHW) periods (i.e., normal summer background conditions), with a maximum UHI difference of 3.30°C and 4.50°C, between HW and NHW periods, in 2012 and 2016 respectively. Our results also show substantial variations in the synergies between UHI and HW due to land use characteristics and synoptic weather conditions; the synergies were relatively more intense in densely built areas and under low wind speed conditions. Our results contribute to our understanding of thermal risks posed by HW in urban areas and, subsequently, the health risks on urban populations. Moreover, they are of significant importance to emergency relief providers as a resource allocation guideline, for instance, regarding which areas and time of the day to prioritize during HW periods in Seoul.

ACS Style

Jack Ngarambe; Jacques Nganyiyimana; Inhan Kim; Mat Santamouris; Geun Young Yun. Synergies between urban heat island and heat waves in Seoul: The role of wind speed and land use characteristics. PLOS ONE 2020, 15, e0243571 .

AMA Style

Jack Ngarambe, Jacques Nganyiyimana, Inhan Kim, Mat Santamouris, Geun Young Yun. Synergies between urban heat island and heat waves in Seoul: The role of wind speed and land use characteristics. PLOS ONE. 2020; 15 (12):e0243571.

Chicago/Turabian Style

Jack Ngarambe; Jacques Nganyiyimana; Inhan Kim; Mat Santamouris; Geun Young Yun. 2020. "Synergies between urban heat island and heat waves in Seoul: The role of wind speed and land use characteristics." PLOS ONE 15, no. 12: e0243571.

Journal article
Published: 02 August 2020 in Journal of Hazardous Materials
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Urban environments face two challenging problems that are parallel in nature but yet with compelling potential synergistic interactions; urban heat island (UHI) and air pollution. We explore these interactions using in-situ temperature and air pollution data collected from 13 monitoring stations for nine years. Through regression analysis and analysis of variance (ANOVA) tests, we found that carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM) show positive correlations with UHI intensity (UHII). At the same time, Ozone (O3) was negatively correlated with UHII. Moreover, there was a substantial seasonal effect on the strength of the correlations between UHI and air pollution, with some air pollutants showing strong associations with UHI during certain seasons (i.e., winter and autumn). The strongest interactions were observed for NO2 (R² = 0.176) and PM10 (R² = 0.596) during the wintertime and for SO2 (R² = 0.849), CO (R² = 0.346), PM2.5 (R² = 0.695) and O3 (R² = 0.512) during autumn. Understanding such interactions is essential for urban climate studies and our study provides a basis for scientific discussions on integrative mitigation strategies for both UHI and air pollution in Seoul city.

ACS Style

Jack Ngarambe; Soo Jeong Joen; Choong-Hee Han; Geun Young Yun. Exploring the relationship between particulate matter, CO, SO2, NO2, O3 and urban heat island in Seoul, Korea. Journal of Hazardous Materials 2020, 403, 123615 .

AMA Style

Jack Ngarambe, Soo Jeong Joen, Choong-Hee Han, Geun Young Yun. Exploring the relationship between particulate matter, CO, SO2, NO2, O3 and urban heat island in Seoul, Korea. Journal of Hazardous Materials. 2020; 403 ():123615.

Chicago/Turabian Style

Jack Ngarambe; Soo Jeong Joen; Choong-Hee Han; Geun Young Yun. 2020. "Exploring the relationship between particulate matter, CO, SO2, NO2, O3 and urban heat island in Seoul, Korea." Journal of Hazardous Materials 403, no. : 123615.

Journal article
Published: 01 June 2020 in Sustainability
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The performance of machine learning (ML) algorithms depends on the nature of the problem at hand. ML-based modeling, therefore, should employ suitable algorithms where optimum results are desired. The purpose of the current study was to explore the potential applications of ML algorithms in modeling daylight in indoor spaces and ultimately identify the optimum algorithm. We thus developed and compared the performance of four common ML algorithms: generalized linear models, deep neural networks, random forest, and gradient boosting models in predicting the distribution of indoor daylight illuminances. We found that deep neural networks, which showed a determination of coefficient (R2) of 0.99, outperformed the other algorithms. Additionally, we explored the use of long short-term memory to forecast the distribution of daylight at a particular future time. Our results show that long short-term memory is accurate and reliable (R2 = 0.92). Our findings provide a basis for discussions on ML algorithms’ use in modeling daylight in indoor spaces, which may ultimately result in efficient tools for estimating daylight performance in the primary stages of building design and daylight control schemes for energy efficiency.

ACS Style

Jack Ngarambe; Amina Irakoze; Geun Young Yun; Gon Kim. Comparative Performance of Machine Learning Algorithms in the Prediction of Indoor Daylight Illuminances. Sustainability 2020, 12, 1 .

AMA Style

Jack Ngarambe, Amina Irakoze, Geun Young Yun, Gon Kim. Comparative Performance of Machine Learning Algorithms in the Prediction of Indoor Daylight Illuminances. Sustainability. 2020; 12 (11):1.

Chicago/Turabian Style

Jack Ngarambe; Amina Irakoze; Geun Young Yun; Gon Kim. 2020. "Comparative Performance of Machine Learning Algorithms in the Prediction of Indoor Daylight Illuminances." Sustainability 12, no. 11: 1.

Journal article
Published: 06 April 2020 in Sustainability
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Improper refrigerant charge amount (RCA) is a recurring fault in electric heat pump (EHP) systems. Because EHP systems show their best performance at optimum charge, predicting the RCA is important. There has been considerable development of data-driven techniques for predicting RCA; however, the current data-driven approaches for estimating RCA suffer from poor generalization and overfitting. This study presents a hybrid deep neural network (DNN) model that combines both a basic DNN model and a thermodynamic model to counter the abovementioned challenges of existing data-driven approaches. The data for designing models were collected from two EHP systems with different specifications, which were used for the training and testing of models. In addition to the data obtained using the basic DNN model, the hybrid DNN model uses the thermodynamic properties as a thermodynamic model. The testing results show that the hybrid DNN model has a prediction performance of 93%, which is 21% higher than that of the basic DNN model. Furthermore, for model training and model testing, the hybrid DNN model has a 6% prediction performance difference, indicating its reliable generalization capabilities. To summarize, the hybrid DNN model improves data-driven approaches and can be used for designing efficient and energy-saving EHP systems.

ACS Style

Jun Kwon Hwang; Patrick Nzivugira Duhirwe; Geun Young Yun; Sukho Lee; HyeongJoon Seo; Inhan Kim; Mat Santamouris. A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps. Sustainability 2020, 12, 2914 .

AMA Style

Jun Kwon Hwang, Patrick Nzivugira Duhirwe, Geun Young Yun, Sukho Lee, HyeongJoon Seo, Inhan Kim, Mat Santamouris. A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps. Sustainability. 2020; 12 (7):2914.

Chicago/Turabian Style

Jun Kwon Hwang; Patrick Nzivugira Duhirwe; Geun Young Yun; Sukho Lee; HyeongJoon Seo; Inhan Kim; Mat Santamouris. 2020. "A Novel Hybrid Deep Neural Network Model to Predict the Refrigerant Charge Amount of Heat Pumps." Sustainability 12, no. 7: 2914.

Journal article
Published: 01 March 2020 in Journal of Green Building
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The application of phase change materials (PCMs) in building envelopes can help promote energy efficiency due to its high heat capacity. Our study aimed to provide energy and economic insights for deploying PCM to buildings in eight different regions of East Asia through a series of energy and economic analysis using computer modelling and simulations. The static payback period (SPP) and dynamic payback (DPP) methods were used to evaluate the economic feasibility of applying a PCM at different melting phase temperatures (20°C, 23°C, 25°C, 27°C and 29°C). Results show that the proper choice of a PCM melting temperature is a key factor to improve the performance of the PCM applied to buildings. A melting phase temperature of 29°C achieved the highest economic feasibility in Seoul, Tokyo; Pyongyang; Beijing; and Ulaanbaatar and a melting temperature of 23°C in Hong Kong had the highest economic feasibility. Overall, the combined economic and energy analysis presented in this study can play an important role in improving the energy and economic feasibility of PCM in buildings.

ACS Style

Abdo Abdullah Ahmed Gassar; Geun Young Yun; Sumin Kim; Choong-Hee Han. ENERGY AND FEASIBILITY ANALYSIS OF APPLYING BIO-BASED PHASE CHANGE MATERIALS TO BUILDINGS IN EAST ASIA. Journal of Green Building 2020, 15, 157 -181.

AMA Style

Abdo Abdullah Ahmed Gassar, Geun Young Yun, Sumin Kim, Choong-Hee Han. ENERGY AND FEASIBILITY ANALYSIS OF APPLYING BIO-BASED PHASE CHANGE MATERIALS TO BUILDINGS IN EAST ASIA. Journal of Green Building. 2020; 15 (2):157-181.

Chicago/Turabian Style

Abdo Abdullah Ahmed Gassar; Geun Young Yun; Sumin Kim; Choong-Hee Han. 2020. "ENERGY AND FEASIBILITY ANALYSIS OF APPLYING BIO-BASED PHASE CHANGE MATERIALS TO BUILDINGS IN EAST ASIA." Journal of Green Building 15, no. 2: 157-181.

Journal article
Published: 26 February 2020 in Scientific Reports
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Urban heat island (UHI), a phenomenon involving increased air temperature of a city compared to the surrounding rural area, results in increased energy use and escalated health problems. To understand the magnitude and characteristics of UHI in Seoul and to accommodate for the high temporal variability and spatial heterogeneity of the UHI which make it inherently challenging to analyze using conventional statistical methods, we developed two deep learning models, a temporal UHI-model and a spatial UHI model, using a feed-forward deep neural network (DNN) architecture. Data related to meteorological elements (e.g. air temperature) and urban texture (e.g. surface albedo) were used to train and test the temporal UHI-model and the Spatial UHI-model respectively. Also, we develop and propose a new metric, UHI-hours, that quantifies the total number of hours that UHI exists in a given area. Our results show that UHI-hours is a better indicator of seasonal UHI than the commonly used index, UHI-intensity. Consequently, UHI-hours is likely to provide a better measure of the cumulative effects of UHI over time than UHI-intensity. UHI-hours will help us to better quantify the effect of UHI on, for example, the overall daily productivity of outdoor workers or heat-related mortality rates.

ACS Style

Jin Woo Oh; Jack Ngarambe; Patrick Nzivugira Duhirwe; Geun Young Yun; Mattheos Santamouris. Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea. Scientific Reports 2020, 10, 1 -13.

AMA Style

Jin Woo Oh, Jack Ngarambe, Patrick Nzivugira Duhirwe, Geun Young Yun, Mattheos Santamouris. Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea. Scientific Reports. 2020; 10 (1):1-13.

Chicago/Turabian Style

Jin Woo Oh; Jack Ngarambe; Patrick Nzivugira Duhirwe; Geun Young Yun; Mattheos Santamouris. 2020. "Using deep-learning to forecast the magnitude and characteristics of urban heat island in Seoul Korea." Scientific Reports 10, no. 1: 1-13.

Review article
Published: 23 January 2020 in Energy and Buildings
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Buildings consume about 40 % of globally-produced energy. A notable amount of this energy is used to provide sufficient comfort levels to the building occupants. Moreover, given recent increases in global temperatures as a result of climate change and the associated decrease in comfort levels, providing adequate comfort levels in indoor spaces has become increasingly important. However, striking a balance between reducing building energy use and providing adequate comfort levels is a significant challenge. Conventional control methods for indoor spaces, such as on/off, proportional-integral (PI), and proportional-integral-derivative (PID) controllers, display significant instabilities and frequently overshoot thermostats, resulting in unnecessary energy use. Additionally, conventional building control methods rarely include comfort regulatory schemes. Consequently, recent research efforts have focused on the use of advanced artificial intelligence (AI) methods to optimize building energy usage while maintaining occupant thermal comfort. We present a review of the current AI-based methodologies being used to enhance thermal comfort in indoor spaces. we focus on thermal comfort predictive models using diverse machine learning (ML) algorithms and their deployment in building control systems for energy saving purposes. We then discuss gaps in the existing literature and highlight potential future research directions.

ACS Style

Jack Ngarambe; Geun Young Yun; Mat Santamouris. The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls. Energy and Buildings 2020, 211, 109807 .

AMA Style

Jack Ngarambe, Geun Young Yun, Mat Santamouris. The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls. Energy and Buildings. 2020; 211 ():109807.

Chicago/Turabian Style

Jack Ngarambe; Geun Young Yun; Mat Santamouris. 2020. "The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls." Energy and Buildings 211, no. : 109807.

Journal article
Published: 12 December 2019 in Science of The Total Environment
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The urban heat island is a vastly documented climatological phenomenon, but when it comes to coastal cities, close to desert areas, its analysis becomes extremely challenging, given the high temporal variability and spatial heterogeneity. The strong dependency on the synoptic weather conditions, rather than on city-specific, constant features, hinders the identification of recurrent patterns, leading conventional predicting algorithms to fail. In this paper, an advanced artificial intelligence technique based on long short-term memory (LSTM) model is applied to gain insight and predict the highly fluctuating heat island intensity (UHII) in the city of Sydney, Australia, governed by the dualistic system of cool sea breeze from the ocean and hot western winds from the vast desert biome inlands. Hourly measurements of temperature, collected for a period of 18 years (1999–2017) from 8 different sites in a 50 km radius from the coastline, were used to train (80%) and test (20%) the model. Other inputs included date, time, and previously computed UHII, feedbacked to the model with an optimized time step of six hours. A second set of models integrated wind speed at the reference station to account for the sea breeze effect. The R2 ranged between 0.770 and 0.932 for the training dataset and between 0.841 and 0.924 for the testing dataset, with the best performance attained right in correspondence of the city hot spots. Unexpectedly, very little benefit (0.06–0.43%) was achieved by including the sea breeze among the input variables. Overall, this study is insightful of a rather rare climatological case at the watershed between maritime and desertic typicality. We proved that accurate UHII predictions can be achieved by learning from long-term air temperature records, provided that an appropriate predicting architecture is utilized.

ACS Style

Geun Young Yun; Jack Ngarambe; Patrick Nzivugira Duhirwe; Giulia Ulpiani; Riccardo Paolini; Shamila Haddad; Konstantina Vasilakopoulou; Mat Santamouris. Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney. Science of The Total Environment 2019, 709, 136068 .

AMA Style

Geun Young Yun, Jack Ngarambe, Patrick Nzivugira Duhirwe, Giulia Ulpiani, Riccardo Paolini, Shamila Haddad, Konstantina Vasilakopoulou, Mat Santamouris. Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney. Science of The Total Environment. 2019; 709 ():136068.

Chicago/Turabian Style

Geun Young Yun; Jack Ngarambe; Patrick Nzivugira Duhirwe; Giulia Ulpiani; Riccardo Paolini; Shamila Haddad; Konstantina Vasilakopoulou; Mat Santamouris. 2019. "Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney." Science of The Total Environment 709, no. : 136068.

Journal article
Published: 23 October 2019 in Renewable Energy
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Predicting the heating and cooling (HC) energy performance of a building is essential in the understanding and energy-efficient control of HC systems. The aims of this study were to develop and propose advanced and accurate energy prediction models using deep learning techniques. Also, to assess the importance and the significance of the relevant variables used in the models. The models were developed based on measured data collected in an educational building and were classified into different prediction time groups at 3-min, 15-min, 30-min, and 1-h time intervals. The inputs used in the models for the HC system and the EHP were selected through a variable selection process based on domain knowledge and correlation analysis. The results also indicate that the operational factors of the HC system had greater influence on the energy consumption than the indoor and outdoor temperatures. The performances of developed models indicate that a deep learning approach can be effectively applied to predict and understand the electric energy consumption of a HC system. Furthermore, the variable selection process and the important variables identified through it can be applied to energy prediction of HC systems in other buildings.

ACS Style

Jun Kwon Hwang; Geun Young Yun; Sukho Lee; HyeongJoon Seo; Matthaios Santamouris. Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system. Renewable Energy 2019, 149, 1227 -1245.

AMA Style

Jun Kwon Hwang, Geun Young Yun, Sukho Lee, HyeongJoon Seo, Matthaios Santamouris. Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system. Renewable Energy. 2019; 149 ():1227-1245.

Chicago/Turabian Style

Jun Kwon Hwang; Geun Young Yun; Sukho Lee; HyeongJoon Seo; Matthaios Santamouris. 2019. "Using deep learning approaches with variable selection process to predict the energy performance of a heating and cooling system." Renewable Energy 149, no. : 1227-1245.

Journal article
Published: 01 October 2019 in Journal of the Korean Solar Energy Society
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ACS Style

Chung-Hoon Zo; Geun-Young Yun. Monthly Heating Energy Needs Analysis According to ISO 13790 and ISO 52016. Journal of the Korean Solar Energy Society 2019, 39, 11 -28.

AMA Style

Chung-Hoon Zo, Geun-Young Yun. Monthly Heating Energy Needs Analysis According to ISO 13790 and ISO 52016. Journal of the Korean Solar Energy Society. 2019; 39 (5):11-28.

Chicago/Turabian Style

Chung-Hoon Zo; Geun-Young Yun. 2019. "Monthly Heating Energy Needs Analysis According to ISO 13790 and ISO 52016." Journal of the Korean Solar Energy Society 39, no. 5: 11-28.

Journal article
Published: 30 August 2019 in Energy and Buildings
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Clothing insulation is a key variable in the prediction of occupant thermal comfort. Consequently, the aim of the current study was to develop predictive models that forecast clothing insulation levels of building occupants. Using field measurements, we investigated the influence of outdoor environment factors and mode of transport on clothing insulation levels of university students. Our results showed that both the mode of transport and weather variables influenced the clothing insulation levels of the students. We then developed a deep neural network model that forecasts mean daily clothing insulation levels using outdoor air temperature at 6 am, dew point temperature at 6 am, gender, season and mode of transport in the based on the collected data from 1316 questionnaire surveys. In addition, we revealed that outdoor environment factors had stronger associations with clothing insulation levels than indoor environment elements. The developed deep neural network model indicated a high R² value of 0.90. In comparison to the deep neural network model, a developed linear model using the same data indicated a lower R² value of 0.698, which implies that the proposed deep neural network model provides an efficient method to forecast clothing insulation levels.

ACS Style

Jack Ngarambe; Geun Young Yun; Gon Kim. Prediction of indoor clothing insulation levels: A deep learning approach. Energy and Buildings 2019, 202, 109402 .

AMA Style

Jack Ngarambe, Geun Young Yun, Gon Kim. Prediction of indoor clothing insulation levels: A deep learning approach. Energy and Buildings. 2019; 202 ():109402.

Chicago/Turabian Style

Jack Ngarambe; Geun Young Yun; Gon Kim. 2019. "Prediction of indoor clothing insulation levels: A deep learning approach." Energy and Buildings 202, no. : 109402.

Journal article
Published: 21 August 2019 in Energy
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Development of energy prediction models plays an integral part in management and enhancement of the energy efficiency of buildings, including carbon emission reduction. Simplified and data-driven models are often the preferred option when detailed information of simulation is not available and the fast responses are required. This study developed data-driven models for predicting electricity and gas consumption in London’s residential buildings at the middle super output areas (MSOA) and lower super output areas (LSOA) with multilayer neural network (MNN), multiple regression (MLR), random forest (RF), and gradient boosting (GB) algorithms, and factors related to socio-demographic, economic, and building characteristics were used as predictors. The results revealed that building characteristics, household income, and the number of households were the most important predictors of electricity and gas consumption. We also found that MNN models have outperformed MLR, RF and GB models in electricity and gas consumption prediction at MSOA and LSOA levels, with R2 values over 0.99 for the electricity consumption model. In summary, this study shows that the MNN models can be a useful tool to assist the formation of energy efficiency policies in buildings at MSOA and LSOA levels.

ACS Style

Abdo Abdullah Ahmed Gassar; Geun Young Yun; Sumin Kim. Data-driven approach to prediction of residential energy consumption at urban scales in London. Energy 2019, 187, 115973 .

AMA Style

Abdo Abdullah Ahmed Gassar, Geun Young Yun, Sumin Kim. Data-driven approach to prediction of residential energy consumption at urban scales in London. Energy. 2019; 187 ():115973.

Chicago/Turabian Style

Abdo Abdullah Ahmed Gassar; Geun Young Yun; Sumin Kim. 2019. "Data-driven approach to prediction of residential energy consumption at urban scales in London." Energy 187, no. : 115973.

Journal article
Published: 23 July 2019 in Energy and Buildings
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Cross-laminated timber (CLT) is a potential option to achieve reductions in carbon emissions and energy consumption. CLT construction systems are environmentally friendly compared to more energy-intensive materials. In this study, the mid-rise apartments with CLT proposed by ASHRAE 90.1 were analyzed for energy consumption. First, the energy consumption of CLT buildings during heating and cooling was analyzed using four types of insulation. For CLT buildings, the airtightness was approximately 0.61 ac/h, (about 0.2 ac/h lower than that of the steel-frame buildings), suggesting that air-tight performance can reduce energy consumption. Energy-saving technology packages were applied to CLT buildings using rock wool insulation, which showed the highest total energy savings of 82.71 MWh, indicating economically advantageous hybrid-insulation. After the analysis of energy consumption according to the type of insulation, the energy retrofit was applied. Second, five energy-saving packages (including energy-saving, heating and cooling, and renewable energy technologies) were applied to two selected CLT buildings to further study energy consumption and energy efficiency. For CLT building using hybrid insulation, Package 3 reduced the total energy consumption by 14.14%, while for CLT building using rock wool insulation, Package 3 had the highest total energy consumption at 12.81%.

ACS Style

Hyun Mi Cho; Ji Hun Park; Seunghwan Wi; Seong Jin Chang; Geun Young Yun; Sumin Kim. Energy retrofit analysis of cross-laminated timber residential buildings in Seoul, Korea: Insights from a case study of packages. Energy and Buildings 2019, 202, 109329 .

AMA Style

Hyun Mi Cho, Ji Hun Park, Seunghwan Wi, Seong Jin Chang, Geun Young Yun, Sumin Kim. Energy retrofit analysis of cross-laminated timber residential buildings in Seoul, Korea: Insights from a case study of packages. Energy and Buildings. 2019; 202 ():109329.

Chicago/Turabian Style

Hyun Mi Cho; Ji Hun Park; Seunghwan Wi; Seong Jin Chang; Geun Young Yun; Sumin Kim. 2019. "Energy retrofit analysis of cross-laminated timber residential buildings in Seoul, Korea: Insights from a case study of packages." Energy and Buildings 202, no. : 109329.

Journal article
Published: 07 March 2019 in Sustainability
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The thermal environment in bedrooms is important for high-quality sleep. Studies confirm that, even during sleep, the human body remains sensitive to the ambient air temperature. This study assesses how changing indoor air temperatures at different sleep stages affects the subjective evaluation of sleep quality. We compare reports from two identical sleeping environments with different thermal control systems: an IoT-based control system that adjusts the indoor air temperature according to the sleep stage and a fixed control system that maintains a constant temperature throughout the night. Ten subjects participated in the experiments and completed a questionnaire about their sleep quality. Our results show that, overall, the subjects experienced better sleep in the room with the IoT-based control system than in the one with a fixed thermal control. The mean differences in sleep satisfaction levels between the two sleeping environments were generally statistically significant in favor of the room with the IoT-based thermal control. Our results thus illustrate the suitability of using the IoT to control the air conditioning in bedrooms to provide improved sleep quality.

ACS Style

Jack Ngarambe; Geun Young Yun; Kisup Lee; Yeona Hwang. Effects of Changing Air Temperature at Different Sleep Stages on the Subjective Evaluation of Sleep Quality. Sustainability 2019, 11, 1417 .

AMA Style

Jack Ngarambe, Geun Young Yun, Kisup Lee, Yeona Hwang. Effects of Changing Air Temperature at Different Sleep Stages on the Subjective Evaluation of Sleep Quality. Sustainability. 2019; 11 (5):1417.

Chicago/Turabian Style

Jack Ngarambe; Geun Young Yun; Kisup Lee; Yeona Hwang. 2019. "Effects of Changing Air Temperature at Different Sleep Stages on the Subjective Evaluation of Sleep Quality." Sustainability 11, no. 5: 1417.

Editorial
Published: 01 August 2018 in Advances in Civil Engineering
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ACS Style

G. Y. Yun; A. Kwok; K. Steemers; W. T. Grondzik. New and Advanced Materials and Technologies in Ultralow-Energy Buildings. Advances in Civil Engineering 2018, 2018, 1 -2.

AMA Style

G. Y. Yun, A. Kwok, K. Steemers, W. T. Grondzik. New and Advanced Materials and Technologies in Ultralow-Energy Buildings. Advances in Civil Engineering. 2018; 2018 ():1-2.

Chicago/Turabian Style

G. Y. Yun; A. Kwok; K. Steemers; W. T. Grondzik. 2018. "New and Advanced Materials and Technologies in Ultralow-Energy Buildings." Advances in Civil Engineering 2018, no. : 1-2.