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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.
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 StyleJack 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 StyleJack 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.
This study evaluated the impact of including a building ceiling depth into a simulation model on skylight efficiency under two climatic conditions (Ulsan and Seoul, South Korea). Using Radiance and EnergyPlus simulation tools integrated in OpenStudio program by National Renewable Energy Laboratory, Golden, Colorado, USA, daylighting and building energy consumption were computed and assessed to evaluate the energy performance and optimization of skylights. Skylight-to-roof ratios from 1% to 25% were analyzed with ceiling depths of 1.5 m to 3 m. The results showed that the range for efficient skylight ratios became smaller with an increase of ceiling depth; in addition, small apertures were more affected by the ceiling depth than were large apertures. Under Ulsan’s climatic conditions, the optimal skylight-to-roof ratios were 8%, 9%, 10%, and 11% for ceiling depths of 1.5 m, 2 m, 2.5 m, and 3 m, respectively. In Seoul, 8% and 9% were the optimum skylight-to-roof ratios for ceiling depths of 1.5 m and 2 m, respectively; no skylight was energy efficient for a ceiling deeper than 2 m. This study indicates that ceiling depth is a critical factor in the evaluation of skylight performance; thus, it should not be excluded from a simulation model, as is often done to simplify simulation modeling.
Amina Irakoze; Young-A Lee; Kee Han Kim. An Evaluation of the Ceiling Depth’s Impact on Skylight Energy Performance Predictions Through a Building Simulation. Sustainability 2020, 12, 3117 .
AMA StyleAmina Irakoze, Young-A Lee, Kee Han Kim. An Evaluation of the Ceiling Depth’s Impact on Skylight Energy Performance Predictions Through a Building Simulation. Sustainability. 2020; 12 (8):3117.
Chicago/Turabian StyleAmina Irakoze; Young-A Lee; Kee Han Kim. 2020. "An Evaluation of the Ceiling Depth’s Impact on Skylight Energy Performance Predictions Through a Building Simulation." Sustainability 12, no. 8: 3117.
Recently, the importance of green building certification in consideration of environmentally friendly factors is being emphasized more when constructing buildings in South Korea. The Green Standard for Energy and Environmental Design (G-SEED) is one of the strategies used by the Korean government to effectively reduce building environmental loads. However, due to the large investment needed to acquire green building certification, building owners, stakeholders, and designers often contemplate how to balance G-SEED certification benefits and the additional costs they involve. Therefore, the purpose of this study was to assess the benefits of G-SEED certification in terms of post-occupancy financial advantages through a comparative analysis of real estate prices of apartments in the Yeongnam area. All of the major factors affecting apartment real estate prices in South Korea were considered, and the real estate price difference between G-SEED certified and non-certified apartments was determined through a one-sample t-test. The results demonstrated that G-SEED certified apartment real estate prices were 9.52% higher than non-certified apartments. This study concluded that G-SEED certification–related investment is worth the additional cost as it increases the real estate value of a building.
Kee Han Kim; Sang-Sub Jeon; Amina Irakoze; Ki-Young Son. A Study of the Green Building Benefits in Apartment Buildings According to Real Estate Prices: Case of Non-Capital Areas in South Korea. Sustainability 2020, 12, 2206 .
AMA StyleKee Han Kim, Sang-Sub Jeon, Amina Irakoze, Ki-Young Son. A Study of the Green Building Benefits in Apartment Buildings According to Real Estate Prices: Case of Non-Capital Areas in South Korea. Sustainability. 2020; 12 (6):2206.
Chicago/Turabian StyleKee Han Kim; Sang-Sub Jeon; Amina Irakoze; Ki-Young Son. 2020. "A Study of the Green Building Benefits in Apartment Buildings According to Real Estate Prices: Case of Non-Capital Areas in South Korea." Sustainability 12, no. 6: 2206.