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

Sustainability

Jack Ngarambe¹  Gon Kim¹  Geun Young Yun¹  Amina Irakoze² 

[1] Department of Architectural Engineering, Kyung Hee University, Yongin 17104, Korea;Department of Architectural Engineering, University of Ulsan, Ulsan 44610, Korea;

关键词: machine learning;    daylighting performance;    daylighting control;    deep learning;    decision trees;    daylight forecasting;   

DOI  :  10.3390/su12114471

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown