21st century Smart building research revealed
24 May 2019
Building efficiency is set to be
significantly transformed with the release of new ground breaking
research undertaken by academics at the SMART Infrastructure Facility at
the
University of Wollongong (UOW), supported by industry partners and a
government agency.
Funded by
Grosvenor Engineering Group (Grosvenor),
Enviro Building
Services (Enviro) and the
NSW Government (through the Department of Industry), the project was
part of SMART’s Digital Living Lab, which provides an
Internet of Things
(IoT) Network to create smarter living in
buildings.
The research achieved three main goals – improve the accuracy of near
real time counting of people in a room/building; accurate forecasting of
indoor temperature to help assess the impact of different load regimes
and model of operations for
HVAC systems; and building a smart sensor
that can predict problems in rotating equipment such as fans.
Senior research fellow Dr Rohan Wickramasuriya led the team. He
comments, “The outcomes were two-fold and are changing how the heating,
ventilation and cooling of buildings can be used more efficiently in the
built
environment. An accurate real time people counting solution for
indoor environments that respects privacy is now available for building
managers to utilise.
“A deep learning-based indoor temperature forecasting algorithm has been
developed which provides a great alternative to the traditional approach
that requires detailed information about a building’s construction,
fit-out and modeled occupancy. Training this algorithm for a new
building is straightforward, hence it will cut time and costs when a
forecasting model is required to predict indoor temperatures.”
“Both Grosvenor and Enviro are leading building services providers that
were open to innovate and collaborate with the university’s
research
facilities to deliver the latest project. The technical skills and
domain knowledge of the research team, as well as strong engagement from
industry partners helped achieve the project’s goals,” he added.
Anonymised real building data was collected from equipment maintained by
Grosvenor, while Enviro’s office spaces provided image data. This data
was used to train deep neural networks to predict the outcomes.
Grosvenor maintains hard technical assets for over 17,000 buildings
across Australia valued at $2.2 billion for major property owners.
Rod Kington, National Sustainability Manager for Grosvenor Engineering
Group, comments, “Making buildings more efficient,
environmentally
sustainable and improving the building value is at the core of what
Grosvenor does. Applying the UOW’s scientific research and real-world
industry experience to the built environment brought solutions to
challenges. This partnership has enabled us to leverage the knowledge
across our extensive building network.
“The accurate detection of building occupation is paramount to keeping
indoor environments within a set comfort zone. Temperatures can vary
dramatically depending on the number of people in a given room. This
research produced a solution that has a much higher accuracy (compared
to the existing solution), allowing building managers to better respond
to cooling and heating demand. Another opportunity includes empowering
tenants with occupancy data to support better space utilisation leasing
decisions.”
“The temperature forecasting model is unique and can predict the
temperature of a room/building within the next 24 hours at 15-minute
intervals and takes into consideration the building occupancy, weather
forecast and historical room temperature. This scenario analysis tool
will significantly reduce overall running costs of a building,” he
added.
Products from the research that are now being used by the industry
include a cost-effective accurate people counter, combining off the
shelf components with powerful machine learning
software. The camera
includes a Raspberry Pi unit and a Raspberry Pi camera module V2. This
unit runs a custom-trained Yolov3 deep learning algorithm. The images
captured by the Raspberry Pi camera are analysed locally using the
Yolvo3 algorithm to count the number of people in the image, even
partially obscured occupants. The processed image is discarded and only
the count is transferred to a database server.
The installed software package includes all data analysis, modelling and
visualisation tasks, including neural networks, all implemented in
Python language.
Dr Rohan Wickramasuriya added, “The research undertaken into deep neural
networks is a future driver of
artificial intelligence in
smart
buildings. We started off with a deep neural network architecture called
YOLO (You Only Look Once) that can detect objects in real time scenes.
“Using transfer learning, we custom trained the YOLO algorithm to detect
people in the images, reviewed annotated images and then evaluated the
accuracy of the algorithm. The test set accuracy of the algorithm was 92
per cent which is an excellent result, given the existing solution’s
accuracy is often only 65 per cent.”
“In addition, to forecast room temperature, we used another deep network
architecture called Long Short Term Memory (LSTM). LSTM models were
compared against classical time series forecasting models and vastly
outperformed the classical models from an accuracy perspective.”
--ENDS--
Source: Grosvenor Engineering Group - www.gegroup.com.au
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