Window Opening Model using Deep Learning Methods

TL;DR

This study develops a deep learning-based window opening model for commercial buildings, trained on extensive data, achieving high accuracy and practical applicability in building performance simulations.

Contribution

It introduces a scalable deep learning model for window opening behavior, addressing bias and tuning issues in existing models, and validates its effectiveness across multiple buildings.

Findings

Achieved 86-89% accuracy and 0.53-0.65 F1 scores in real buildings.

Model performance drops by 15% with sparse input data but maintains high F1 scores.

Largest dataset used for window state modeling with 20 million data points.

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 86-89 % and 0.53-0.65 respectively. The performance dropped around 15 % points in case of sparse input data, while the F1 score remained high.