Comparative Performance of Machine Learning Algorithms in the Prediction of Indoor Daylight Illuminances

Jack Ngarambe, Amina Irakoze, Geun Young Yun and Gon Kim
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Jack Ngarambe: Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Korea
Amina Irakoze: Department of Architectural Engineering, University of Ulsan, Ulsan 44610, Korea
Geun Young Yun: Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Korea
Gon Kim: Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Korea

Sustainability, 2020, vol. 12, issue 11, 1-22

Abstract: 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 (R 2 ) 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 (R 2 = 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.

Keywords: machine learning; daylighting performance; daylighting control; deep learning; decision trees; daylight forecasting; predictive modeling; time-series (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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