Incorporate accurate time series forecasting to solve real-world problems
By Ms Nancy Van Nieuwenhove | 4th May 2020
2nd Year PhD candidate, Bsc (Hons) in Engineering, BCS
Research interests: machine learning, time series forecasting, optimisation, simulation, modelling
Rakshitha Godahewa is originally from Sri Lanka and has always wanted to become an engineer. Her dream came true when she won the very competitive GCE Advanced Level examination in Sri Lanka. She passed as one of the top 4 students in her district and qualified to enter the Faculty of Engineering, University of Moratuwa. She majored in computer science and engineering, won two awards from University of Moratuwa for academic excellence, completed her degree with a first class honours and was one of the top 4 students in the class of 2016. Meanwhile, she also completed the British Computer Society (BCS) degree. In the final year of her bachelor’s degree, she did a machine learning research project where she used Visitor Location Registry (VLR) data for short-term traffic prediction. The research findings were presented at the Netmob Conference, Italy 2017. There, she found an interest towards machine learning and Artificial Intelligence (AI) research.
After completing her degree, she worked for Codegen International (Pvt) Ltd, as a Senior Software Engineer for 3 years and applied problem-solving principles to help the sector. She dealt with travel-related bookings, built web-based products and gained analytical and programming skills as well as knowledge in algorithms. She realised she could contribute greatly to the engineering field by becoming a researcher. “After browsing through the Monash University website, after seeing the number of researchers and their invaluable contribution to the community, I contacted Professor Geoff Webb and Dr Christopher Bergmeir with my research and they took me on as a PhD candidate in February 2019.“
During the first few months of her PhD, she developed a new classifier that supports accurate high-dimensional stream classification. Her research paper on the subject was recently accepted to be presented at the International Joint Conference on Neural Networks (IJCNN), UK, 2020.
Rakshitha’s PhD research mainly focuses on improving the accuracy of time series forecasting using global ensemble models; like Recurrent Neural Networks (RNN), Feed-Forward Neural Networks (FFNN) and Pooled Regression models. “We predict the future of time series via different machine learning techniques and increase the forecasting accuracy by optimally aggregating a set of forecasting models. My main goal is to use these models with electricity demand forecasting and energy forecasting. Accurate time series forecasting can be very beneficial for any businesses and industries. Currently, we are using the Ausgrid dataset with our experiments which represents the energy consumption of Australian households”.
She has already started using these forecasting models with real-world applications. She is working on a project with Honeywell Ltd where she uses temperature forecasts provided by an RNN model to optimise the energy consumption of air conditioning systems especially during the unoccupied periods of lecture theatres. “Often the AC is on in lecture theatres even if no lectures are happening. We are simulating and predicting the future inside temperatures of lecture theatres using machine learning technologies and later use them to optimise the energy consumption of air-conditioning systems. We hope to do a validation of our project in a near future. The conclusions of this research are valid to similar building rooms including hotel conference rooms and libraries, so they have high commercial benefits”.
In 2019, Rakshitha and Dr Arnaud Prouzeau presented their findings at the Honeywell Users Group Conference, in Sydney Australia. “Arnaud focused on the immersive visualisation part of our research and I presented the machine learning part. We received many positive comments from internationally based researchers. I also presented our findings (poster format) at the Monash Energy Conference with Chang Deng, Masters student in IT".
Furthermore, she recently participated in a forecasting competition conducted by Decathlon and was able to obtain the first position. This year, Rakshitha received a scholarship from the Monash University Grid Innovation Hub for her contribution towards energy optimisation research. She notes that all these achievements wouldn't be possible without the endless support and guidance from her supervisors.
In 2020, Rakshitha would like to see more research towards energy optimisation. “The world has a limited amount of energy sources. So, there is a need to increase the research towards renewable energy and energy optimisation. I would also like to see more collaborations between Monash University and industry partners towards energy optimisation research.”
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