Accurate building occupancy estimation with inhomogeneous Markov chain
The energy consumption of a high-rise structure increases as the urban population grows. The knowledge of a building's occupancy is critical since it has a significant impact on the building's energy usage. To avoid the situation worsening, it is critical to create an occupancy model...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/157505 |
| _version_ | 1811685030978650112 |
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| author | Lee, Jun Hao |
| author2 | Soh Yeng Chai |
| author_facet | Soh Yeng Chai Lee, Jun Hao |
| author_sort | Lee, Jun Hao |
| collection | NTU |
| description | The energy consumption of a high-rise structure increases as the urban population
grows. The knowledge of a building's occupancy is critical since it has a significant
impact on the building's energy usage. To avoid the situation worsening, it is critical
to create an occupancy model to address the issue of building energy efficiency.
While there are several methods for estimating a building's occupancy, each has its
own set of disadvantages. As a result, a less invasive and more accurate method for
assessing interior occupancy is critical.
This work studies the use of an inhomogeneous Markov chain to anticipate
occupancy in a multi-occupant single zone (MOSZ) situation. This experiment's
MOSZ scenario is restricted to an NTU Hive lecture room. The number of occupants
in the room is counted by PIR sensors and the counts will be served as the states of
the Markov chain.
Based on the room's actual occupancy measurements, MATLAB simulations are
conducted to forecast occupancy. The model's performance is measured using the
mean occupancy, the initial arrival time, the continuous occupation time, the number
of high occurrences, and the transitions between vacant and occupied states. The
normalized root mean square error (NRSME) is used to assess the model's
performance. The result of initial arrival and continuous occupation duration are
positive, but the others are not that good. These difficulties may be addressed with a
larger dataset, and with corrections to the MATLAB code. |
| first_indexed | 2024-10-01T04:38:03Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/157505 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:38:03Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1575052023-07-07T19:16:26Z Accurate building occupancy estimation with inhomogeneous Markov chain Lee, Jun Hao Soh Yeng Chai School of Electrical and Electronic Engineering EYCSOH@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems The energy consumption of a high-rise structure increases as the urban population grows. The knowledge of a building's occupancy is critical since it has a significant impact on the building's energy usage. To avoid the situation worsening, it is critical to create an occupancy model to address the issue of building energy efficiency. While there are several methods for estimating a building's occupancy, each has its own set of disadvantages. As a result, a less invasive and more accurate method for assessing interior occupancy is critical. This work studies the use of an inhomogeneous Markov chain to anticipate occupancy in a multi-occupant single zone (MOSZ) situation. This experiment's MOSZ scenario is restricted to an NTU Hive lecture room. The number of occupants in the room is counted by PIR sensors and the counts will be served as the states of the Markov chain. Based on the room's actual occupancy measurements, MATLAB simulations are conducted to forecast occupancy. The model's performance is measured using the mean occupancy, the initial arrival time, the continuous occupation time, the number of high occurrences, and the transitions between vacant and occupied states. The normalized root mean square error (NRSME) is used to assess the model's performance. The result of initial arrival and continuous occupation duration are positive, but the others are not that good. These difficulties may be addressed with a larger dataset, and with corrections to the MATLAB code. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-05-11T00:48:00Z 2022-05-11T00:48:00Z 2022 Final Year Project (FYP) Lee, J. H. (2022). Accurate building occupancy estimation with inhomogeneous Markov chain. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157505 https://hdl.handle.net/10356/157505 en A1119-211 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering::Computer hardware, software and systems Lee, Jun Hao Accurate building occupancy estimation with inhomogeneous Markov chain |
| title | Accurate building occupancy estimation with inhomogeneous Markov chain |
| title_full | Accurate building occupancy estimation with inhomogeneous Markov chain |
| title_fullStr | Accurate building occupancy estimation with inhomogeneous Markov chain |
| title_full_unstemmed | Accurate building occupancy estimation with inhomogeneous Markov chain |
| title_short | Accurate building occupancy estimation with inhomogeneous Markov chain |
| title_sort | accurate building occupancy estimation with inhomogeneous markov chain |
| topic | Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | https://hdl.handle.net/10356/157505 |
| work_keys_str_mv | AT leejunhao accuratebuildingoccupancyestimationwithinhomogeneousmarkovchain |