Occupancy Prediction in Buildings Using Cascaded LSTM Model

Buildings are one of the prominent sectors among global primary energy consumption. A large portion of this energy consumption is influenced by occupancy interaction with the buildings. Occupancy prediction in buildings without intruding their privacy helps to enhance the building energy management. Due to the complex relations of the inputs and the temporal dependency, modeling accurate occupancy predictions is highly difficult. The use of Deep Learning (DL) algorithms is one of the best approaches for accomplishing this goal. This paper provides an analysis using horizontally cascaded Long Short-Term Memory (LSTM) model as a baseline for occupancy prediction. The proposed horizontally cascaded LSTM model focuses on learning local patterns and dependencies within their input sequences, and both short term and long terms dependencies in temporal direction along with the relation between other input features, allowing for a more comprehensive understanding of the input data. This architecture can capture a broader range of information from the collected building data and learn more heterogeneity in occupancy presence behavior. The models are also compared for different prediction window sizes. The OPTUNA optimization is utilized for hyper-parameter tuning and to determine number of LSTMs to be cascaded. The proposed models function better for smaller window size and optimization of number of cascaded LSTMs are essential for improving the accuracy of the model. The paper also shows that for window sizes, 2–4 LSTMs are optimal to cascade.