Indoor occupancy estimation using environmental parameters
In this work, we address the problem of obtaining reliable estimates of the real-time occupancy for Air-Conditioning and Mechanical Ventilation (ACMV) systems. We take a non-intrusive approach that is gaining popularity, which is the use of environmental parameters such as CO2, temperature, humi...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Thesis-Doctor of Philosophy |
Language: | English |
Published: |
Nanyang Technological University
2017
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/72520 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Summary: | In this work, we address the problem of obtaining reliable estimates of
the real-time occupancy for Air-Conditioning and Mechanical Ventilation
(ACMV) systems. We take a non-intrusive approach that is gaining popularity,
which is the use of environmental parameters such as CO2, temperature,
humidity and pressure. The approach requires the extraction and
selection of useful features from the sensor data, which can be used with
machine learning techniques to obtain occupancy estimates. To date, occupancy
estimation has primarily focused on static features, which we define to
be features sampled instantaneously, as opposed to dynamic features that
are sampled over a moving window. In this work, we propose novel feature
selection techniques that employ both static and dynamic features. We
study the performance of these algorithms with experimental data collected
through a multi-sensory network in an office space.
First, we propose novel feature selection algorithms within the static feature
paradigm. In past works, feature selection has generally been implemented
using filter-based approaches. In this work, we introduce the use of wrapper
and hybrid feature selection for occupancy estimation. Compared to
filter methods, our approach can achieve a better occupancy estimation accuracy.
Additionally, we use a ranking-based combinatorial search in our
algorithms, which is more efficient than the exhaustive search used in past
works. For wrapper feature selection, we propose the WRANK-ELM, which
searches an ordered list of features using the Extreme Learning Machine
(ELM) classifier. For hybrid feature selection, we propose the RIG-ELM,
which is a filter-wrapper hybrid that uses the Relative Information Gain
(RIG) criterion for feature ranking and the ELM for a combinatorial search.
Experimental results for different sensor positions and different time resolutions
show that the proposed algorithms outperform past work in terms of
accuracy and computation time. However, the performance is not so smooth
for the complex occupancy profile at a 1-minute time resolution.
Dynamic features are more informative than static features, but their use in
occupancy estimation has to date been very limited. In this work, we introduce
the Feature-Scaled ELM (FS-ELM), which is an ELM-based estimator
that uses dynamic features. The FS-ELM is in fact a novel architecture of
the ELM, in which a feature layer is added to the standard ELM. The feature
layer extracts dynamic features from the raw data. Additionally, we present
an effective smoothing strategy to address the problem of noise in environmental
parameter data. We demonstrate through experimental results that
the FS-ELM yields excellent occupancy estimation accuracy, while retaining
the computational efficiency of the standard ELM.
Also in this work, we propose a generalized feature selection framework for
constructing an occupancy estimator for dynamic features. The framework
is a kind of filter-wrapper hybrid feature selection method, which is novel in
that it uses a combination of static and dynamic features. In the framework,
the filter component works with static features, while the wrapper component
works with dynamic features. We use the static features for purposes
of speed, since filter methods of feature selection (which work with static
features) are quite fast. Thus, the overall computation time of the framework
is kept low, while ensuring good accuracy of estimation due to the use
of dynamic features. The framework thus offers a reliable method of evaluating
a large set of features. To perform occupancy estimation within the
proposed framework, we present a novel technique called the Hybrid Feature-
Scaled Extreme Learning Machine (HFS-ELM). The HFS-ELM is a dynamic
model of the occupancy level in which the present occupancy depends on
the measurements of multiple environmental parameters and the estimated
occupancy level in a past time horizon. It is thus a generalized version of the
FS-ELM, which works with only one type of environmental parameter. Also,
while the FS-ELM is limited to two types of arbitrarily selected features, the
HFS-ELM accommodates multiple feature types. Through experimental results,
we show the excellent performance of the HFS-ELM even for complex
occupancy profiles at a 1-minute time resolution. |
---|