Supervised by Professor Tong Zhang at the HKUST and Dr Lee Cheuk-wing, Binnie’s thesis proposed combining Professor Zhang’s machine learning algorithm, Regularised Greedy Forest, with two other machine learning applications, creating an ensemble model that delivered improved stability and greater accuracy of short-term forecasting.
With cross departmental support, Binnie tested and implemented the ensemble model from 2021. The model worked well and reduced errors in forecasting by an average of 20% compared with the previous model.
From early 2022, the model was applied to CLP Power’s daily operations where it quickly established itself as an effective way to reduce fuel waste, increase operational performance and support decarbonisation.
A Lifelong Learning Curve
Accurate load forecasting not only saves costs and reduces emissions but also boosts energy efficiency and improves the overall reliability of the power system. Similarly, Binnie’s work has not only benefitted CLP Power but has also brought valuable insights for the industry.
Binnie last summer completed his dual degree programme in less than three years, becoming the first graduate to receive a Master of Philosophy in computer science and engineering from the HKUST, together with a master’s degree with distinction in future energy and power system smart operation and management from the University of Strathclyde, an achievement that brought him the Best MSc Student Award from the university in Scotland.
Binnie says he was motivated by the ability to apply his knowledge to tackle real-world problems. His dedication to his profession, together with the encouragement and support of his parents, colleagues and supervisors throughout the course, ultimately resulted in a breakthrough that has led to a more reliable power supply.
Perseverance is the key both to his own achievement and the machine learning model he has created. Both are constantly seeking to improve themselves to perform as effectively as possible, he adds.