Deep Learning Based Prediction Towards Designing A Smart Building Assistant System

Nowadays, smart building infrastructures are equipped with hundreds of sensors to monitor building environments and provide smart solutions for occupant comfortability and energy efficiency. Ideally, an automated system can predict and adjust the physical features (e.g., lighting, air quality, temperature, and so on) in a person’s office based on his/her personalized preferences and activities. However, since the data is from one person, there may not be sufficient data for machine learning model training, and the data’s quality may be low (e.g., with noises). Then, it is a challenge to conduct accurate predictions to provide personalized environment adjustment. To handle this problem, in this paper, we propose a smart building assistance system consisting of different sensor data analysis approaches and a deep neural network (DNN)-based prediction model to make a more accurate prediction despite low-quality sensor data. First, we collected a year-long smart building dataset from four different data sources (i.e., sensors, calendar, weather, and survey). Second, we perform different feature engineering approaches (i.e., concretization, one-hot encoding, and multiple feature combination) on the data as inputs for the prediction models. Third, we identify a support vector regression-based prediction model and propose a hybrid DNN model consisting of several recurrent neural network blocks and a feed-forward DNN block to predict different preferred physical features considering different activities of a person (e.g., meeting, lunch, research activities). Finally, we conduct experimental studies to evaluate the performance of the proposed prediction models compared to other existing machine learning models in terms of accuracy. Our predicted preferred physical features match the occupant’s preferred ranges of different physical features during a specific activity. We also open-sourced our code on GitHub.