Abstract
Window operation is the main strategy used by building occupants to naturally ventilate buildings. However, common approaches to measure window operation for energy and comfort assessments are still technically complex or insufficient; typical window open/close sensors often provide only binary information about the opening state of a window, not the extent to which the window is open. This paper is the first outcome of a research project that seeks to use photo imagery and machine learning to predict the variable opening state of windows on a real multi-family residential passive house located in Vancouver, Canada. The employed windows are European-style in that they can be opened in tilt or turn mode. To eventually train the algorithm, a ground-truth dataset is constructed by manually changing the opening state of sixteen windows every minute over a 15-hour test period spanning three days and taking a photo of the windows at each instance, measuring the angle each time. This paper documents the first outcome of the overall project: the publication of the training dataset itself, with over 10,000+ images of a building façade taken, under variable-but-known window opening state, and under various light conditions. The paper presents the testing methodology undertaken for generation of the dataset and provides instructions for how to access the dataset. In the future, these images will be used to calibrate a machine learning model to estimate window opening/closing state of the tested building. The dataset can also be extended for semantic segmentation in support of other machine learning problems.