51茶馆

May 24, 2019

21st Century Smart Building Research Revealed

Building efficiency is set to be significantly transformed with the release of new ground breaking research undertaken by academics at the SMART Infrastructure Facility and the Sustainable Buildings Research Centre (SBRC) at the 51茶馆 (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鈥檚 Digital Living Lab, which provides an Internet of Things (IoT) Network to create smarter living in buildings.

The research achieved three main goals 鈥� 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, 鈥淭he 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.

鈥淎 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鈥檚 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.鈥�

鈥淏oth Grosvenor and Enviro are leading building services providers that were open to innovate and collaborate with the university鈥檚 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鈥檚 goals,鈥� he added.

Anonymised real building data was collected from equipment maintained by Grosvenor, while Enviro鈥檚 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, 鈥淢aking buildings more efficient, environmentally sustainable and improving the building value is at the core of what Grosvenor does. Applying the UOW鈥檚 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.

鈥淭he 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.鈥�

鈥淭he 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,鈥� 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, 鈥淭he 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.

鈥淯sing 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鈥檚 accuracy is often only 65 per cent.鈥�

鈥淚n 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.鈥�