Data-Driven Thermal Comfort Prediction With Support Vector Machine Conference Paper uri icon

abstract

  • Personal thermal comfort is a crucial yet often over-simplified factor in building climate control. Traditional comfort models lack the adaptability to fit individuals demand. Recent advances of machine learning and ubiquitous sensor networks enable the data-driven approach of thermal comfort. In this paper, we built a platform that can simulate occupants with different thermal sensations and used it to examine the performance of support vector machine (SVM) and compared with several other popular machine learning algorithms on thermal comfort prediction. We also proposed a hybrid SVM-LDA thermal comfort classifier that can improve the efficiency of model training.

name of conference

  • Volume 3: Manufacturing Equipment and Systems

published proceedings

  • Volume 3: Manufacturing Equipment and Systems

author list (cited authors)

  • Peng, B. o., & Hsieh, S.

citation count

  • 11

complete list of authors

  • Peng, Bo||Hsieh, Sheng-Jen

publication date

  • June 2017