Occupancy Forecasting using LSTM Neural Network and Transfer Learning

Leeraksakiat, Piyapat; Pora, Wanchalerm

doi:10.1109/ecti-con49241.2020.9158103

Cited by 12 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This network is unable to predict another person's behavior accurately. To cope with this problem, Leeraksakiat and Pora [33] applied LSTM to enhance the power of the network when a person occupies a place or changes their comfort, or when a new person enters the place. First, they used a norm dataset to train the network, and then new batches of sampling data were added to update the network, i.e., transferring new knowledge to the previous information.…”

Section: Related Workmentioning

confidence: 99%

Indoor Occupancy Detection Based on Environmental Data Using CNN-XGboost Model: Experimental Validation in a Residential Building

2022

View full text Add to dashboard Cite

Indoor occupancy prediction can play a vital role in the energy-efficient operation of building engineering systems and maintaining satisfactory indoor climate conditions at the lowest possible energy use by operating these systems on the basis of occupancy data. Many methods have been proposed to predict occupancy in residential buildings according to different data types, e.g., digital cameras, motion sensors, and indoor climate sensors. Among these proposed methods, those with indoor climate data as input have received significant interest due to their less intrusive and cost-effective approach. This paper proposes a deep learning method called CNN-XGBoost to predict occupancy using indoor climate data and compares the performance of the proposed method with a range of supervised and unsupervised machine learning algorithms plus artificial neural network algorithms. The comparison is performed using mean absolute error, confusion matrix, and F1 score. Indoor climate data used in this work are CO2, relative humidity, and temperature measured by sensors for 13 days in December 2021. We used inexpensive sensors in different rooms of a residential building with a balanced mechanical ventilation system located in northwest Copenhagen, Denmark. The proposed algorithm consists of two parts: a convolutional neural network that learns the features of the input data and a scalable end-to-end tree-boosting classifier. The result indicates that CNN-XGBoost outperforms other algorithms in predicting occupancy levels in all rooms of the test building. In this experiment, we achieved the highest accuracy in occupancy detection using inexpensive indoor climate sensors in a mechanically ventilated residential building with minimum privacy invasion.

show abstract

Section: Related Workmentioning

confidence: 99%

Indoor Occupancy Detection Based on Environmental Data Using CNN-XGboost Model: Experimental Validation in a Residential Building

2022

View full text Add to dashboard Cite

show abstract

“…In this research, a pre-trained model from the source domain called Convolutional Deep Long Short-Term Memory (CDLSTM) has been used to transfer knowledge to the target domain by sharing model weights. [29] has also a deep transfer learning method based on LSTM using data collected from IoT sensors such as thermometer, PIR, and CO2 sensors. The considered approach, which aims to reduce energy consumption, transfers knowledge by sharing the source model weights to the target model and by retraining the target classifier with a small amount of target data.…”

Section: Occupancy Estimationmentioning

confidence: 99%