Window opening model using deep learning methods. (November 2018)

• Record Type: Journal Article
• Authors: Markovic, Romana; Grintal, Eva; Wölki, Daniel; Frisch, Jérôme; van Treeck, Christoph
• Is Part Of: Building and environment. Volume 145(2018)
• Page Start: 319
• Page End: 329
• Publication Date: 2018-11
• Subjects: deep learning -- Neural networks -- Occupant behavior -- Window opening -- Natural ventilation; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2018.09.024
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Occupant behavior (OB) and in particular window openings need to be considered in building performance simulation (BPS), in order to realistically model the indoor climate and energy consumption for heating ventilation and air conditioning (HVAC). However, the proposed OB window opening models are often biased towards the over-represented class where windows remained closed. In addition, they require tuning for each occupant which can not be efficiently scaled to the increased number of occupants. This paper presents a window opening model for commercial buildings using deep learning methods. The model is trained using data from occupants from an office building in Germany. In total, the model is evaluated using almost 20 mio. data points from 3 independent buildings, located in Aachen, Frankfurt and Philadelphia. Eventually, the results of 3100 core hours of model development are summarized, which makes this study the largest of its kind in window states modeling. Additionally, the practical potential of the proposed model was tested by incorporating it in the Modelica-based thermal building simulation. The resulting evaluation accuracy and F1 scores on the office buildings ranged between 86 and 89% and 0.53–0.65 respectively. The performance dropped around 15% points in case of sparse input data, while the F1 score remained high. Highlights: Narrow network architecture (5 hidden layers) led to an optimal performance. Improved F1 score, when compared to alternative models calibrated on building level. Co-simulation of deep learning driven OB model with Modelica Dymola. Scale adaptation of OB data identified as an open research question.