| Title |
Development of Occupancy Prediction Model and Performance Comparison According to the Recurrent Neural Network Models |
| Authors |
최영재(Choi, Young Jae) ; 박보랑(Park, Bo Rang) ; 현지연(Hyun, Ji Yeon) ; 문진우(Moon, Jin Woo) |
| DOI |
https://doi.org/10.5659/JAIK.2022.38.10.231 |
| Keywords |
Occupancy forecasting; recurrent neural network; occupant-centric control |
| Abstract |
An accurate occupancy prediction is essential for occupant-centric control (OCC) that saves energy while providing a comfortable indoor
environment. Various machine learning-based approaches are being tried to develop an occupancy prediction model. Among these approaches,
the performance of the recurrent neural network (RNN) based models, showed strength in time series forecasting and were found to be
superb. However, studies related to performance comparison between RNN based models are insufficient; although the model performance had
possibility for improvement through optimization. Therefore, in this study the RNN, long short-term memory (LSTM), and gated recurrent unit
(GRU) models were developed to predict the number of occupants after 15, 30, and 60 minutes. The optimal models for each prediction
horizon were derived through optimization and performance evaluation. As a result, the GRU model presented the best performance. The root
mean squared error (RMSE) and mean absolute error (MAE) of the prediction model after 15 minutes was 0.8073, 1.5301, the prediction
model after 30 minutes was 1.2841, 2.3386, and 2.0769, 3.3685, for the prediction model after 60 minutes. These results show superior
performance compared to the existing RNN based models and signify that it is possible to provide accurate values for various prediction
horizons. Thus, if outlier supplementation and addition of the adaptation function are implemented through an algorithm in the future, the
developed models are expected to be utilized as a key element for OCC. |