Building occupancy analytics based on deep learning through the use of environmental sensor data
| dc.contributor.author | Zhang, Zheyu | en |
| dc.contributor.committeechair | Rahman, Saifur | en |
| dc.contributor.committeemember | Yu, Guoqiang | en |
| dc.contributor.committeemember | Ampadu, Paul K. | en |
| dc.contributor.department | Electrical Engineering | en |
| dc.date.accessioned | 2023-05-25T08:00:28Z | en |
| dc.date.available | 2023-05-25T08:00:28Z | en |
| dc.date.issued | 2023-05-24 | en |
| dc.description.abstract | Balancing indoor comfort and energy consumption is crucial to building energy efficiency. Occupancy information is a vital aspect in this process, as it determines the energy demand. Although there are various sensors used to gather occupancy information, environmental sensors stand out due to their low cost and privacy benefits. Machine learning algorithms play a critical role in estimating the relationship between occupancy levels and environmental data. To improve performance, more complex models such as deep learning algorithms are necessary. Long Short-Term Memory (LSTM) is a powerful deep learning algorithm that has been utilized in occupancy estimation. However, recently, an algorithm named Attention has emerged with improved performance. The study proposes a more effective model for occupancy level estimation by incorporating Attention into the existing Long Short-Term Memory algorithm. The results show that the proposed model is more accurate than using a single algorithm and has the potential to be integrated into building energy control systems to conserve even more energy. | en |
| dc.description.abstractgeneral | The motivation for energy conservation and sustainable development is rapidly increasing, and building energy consumption is a significant part of overall energy use. In order to make buildings more energy efficient, it is necessary to obtain information on the occupancy level of rooms in the building. Environmental sensors are used to measure factors such as humidity and sound to determine occupancy information. However, the relationship between sensor readings and occupancy levels is complex, making it necessary to use machine learning algorithms to establish a connection. As a subfield of machine learning, deep learning is capable of processing complex data. This research aims to utilize advanced deep learning algorithms to estimate building occupancy levels based on environmental sensor data. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:37396 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/115178 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Building Energy Management | en |
| dc.subject | Internet of Things (IoT) | en |
| dc.subject | Deep learning algorithms (LSTM | en |
| dc.subject | Attention) | en |
| dc.subject | Carbon Dioxide (CO2) | en |
| dc.title | Building occupancy analytics based on deep learning through the use of environmental sensor data | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Electrical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |
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