Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/64311
Title: Occupancy estimation in indoor environments
Authors: Chen, Constance Xiangxing
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2015
Abstract: Building energy efficiency is a rising issue that attracts many organisations to achieve sustainability in buildings. In order to achieve this goal, occupancy levels in rooms are detected to optimize the performance of HVAC systems. The effect of air quality, due to the presence of occupants, has a direct effect using indices such as humidity, temperature and CO2 levels. This report summarises the setup of the hardware and Raspberry Pi for data collection and storage. Incorporating with the sensor nodes, CO2 levels, temperature, pressure, humility, altitude and airflow for analysis of the data, as well as a camera to capture actual occupancy. The data collected will be analysed by training Extreme Learning Machine (ELM) and Multi-layer Perceptron (MLP) to provide an estimated occupancy in real time. By making comparison between the two machine learning techniques using distinct features, we will determine which method provides a better indicator in estimating occupancy level.
URI: http://hdl.handle.net/10356/64311
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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