School of
Information Technology and Electrical Engineering

Speaker: Mr Ruiyuan Zhang
Seminar Date: Wed, 28/02/2018 - 10:00
Venue: 49-502; AEB Seminar Room
Host: Prof Tapan Saha; Prof Xioafang Zhou

Seminar Type:  PhD Confirmation Seminar


Nowadays, the power systems become one of the most interesting prospective scenarios of data analysis among all industries. One important reason is that the data in power systems have unique characteristics compared with other fields. The challenges in developing “Big Data” applications in power distribution system are two-fold: firstly, we need to design a flexible system architecture that accommodates and optimizes big data analytics workloads. Secondly, we need to develop scalable mathematical tools capable of analyzing data as well.

Forecasting is one of the most important application of data analytics. Especially, the very short term forecasting is in demand. Because solar data comprise strong periodicity and randomness simultaneously, which causes traditional forecasting approaches are not feasible in some cases. In this confirmation presentation we will make a review of existing solar forecasting techniques and propose a nearest-neighbour search based forecasting method for solar energy forecasting. The experiments and evaluation results will be demonstrated as well.


Mr Ruiyuan Zhang obtained a B. Sc in Electrical Engineering from Tongji University, Shanghai in 2012 and an M.Sc in Electrical Power Engineering from RWTH Aachen University, Germany in 2015.

Currently, he is a PhD student at the School of ITEE, the University of Queensland. His research interests include renewable energy forecasting, data mining and related topics.