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  • [ November 06, 2018]

    Computational Intelligence in Smart Power Grid Management and Energy Feasibility Studies

  • Speaker:
    Ravinesh Deo
    Date:
    November 07, 2018
    Time:
    10:00am
    Location:
    Room 210, COE Building No.1
    Host:
    Jianchun Mi
  • Abstract
  • Today, big data is a significant matter! This seminar will discuss research in predictive modelling with artificial intelligence. It will describe briefly big data principles, with less technical detail but a greater focus on applications and result. In application space it will provide case studies in recent papers demonstrating the merits of advanced data analytic models in real-life, particularly in energy management systems. Models considered will include, but not limited to, deep learning, extreme learning machines(极限学习机器), artificial neural network(人工神经网络), support vector machines(支持向量机器), multivariate adaptive regression spline (多元自适应回归样条)and M5 Tree, whereas model optimisation tools will include the results obtained by applying meta-heuristic feature selection (元启发式特征选择)(or ‘search’) algorithms, feature weight optimisation (or ‘add-in’) algorithms and multi-resolution tools such as empirical mode decomposition applied to model data to improve the prediction. In particular, feature selections are required to screen optimal inputs, improve the accuracy and reduce the computational burden, whereas add-in algorithms can help extract most, if not all of the predictive features from a large pool of carefully screened input variables. Empirical mode decompositions, can assist in identifying the frequency components in model inputs and addressing issues of non-stationarity, trends, jumps and periodicities present in model design data. This seminar will reveal the importance of ancillary tools in predictive modelling with applications of artificial intelligence models in energy demand management and solar energy simulations. The seminar will discuss and expect to exchange ideas and future challenges that we as, researchers, face in predictive modelling that must be considered in practical energy management models that are used in real-life simulations to design decision systems for energy management with big data analytics.
  • Biography
  • Dr Ravinesh Deo, is an Australia-China Young Scientist Award recipient, IEEE Senior Member and Senior Lecturer. Dr Deo’s research develops decision-systems for energy and environment and has published over 130 peer reviewed papers (including 100 journal papers). Designing data smart systems with heuristic and metaheuristic algorithms and improving predictive systems is central to his research with interest in deep learning, convolutional neural and long- short-term memory network. Dr Deo’s professional practice is through scientific bodies: IEEE, Institute of Physics, Australian Mathematical Society, Australasian Association for Engineering Education, Australia Global Alumni and American Geophysical Union. Dr Deo’s research was highly recognized through Queensland Government U.S. Smithsonian Research Fellowship 2018, four Publication Excellence Prizes (2016-2018), Japan Society for Promotion of Science Fellowship (2017), Head of Department Award, Chinese Academy of Science Presidential Fellowship (2016) and Australian Endeavour Fellowship (2015). Dr Deo has a BSc, with a Gold Medal, MSc from University of Canterbury and PhD from University of Adelaide supervised by Professor Jianchun Mi (Jamie) (now at Peking University) and Professor Graham Nathan (Adelaide).

     

    Dr Deo’s postdoc work at The University of Queensland was published in Global Change Biology, Geophysical Research Letters and a highlight in Nature, investigated climate impacts of land cover change to show deforestation as a trigger for drought. Dr Deo undertook knowledge exchange programs in Japan, Europe, China, USA, Singapore and Canada. As leader of Environmental Modelling and Simulation Group, Dr Deo mentors over 15 postgraduate students and has published in Renewable and Sustainable Energy Reviews (Scopus Impact 9.05) and Applied Energy (7.20). Supervising masters and doctoral programs, Dr Deo’s research is recognised internationally, with Google Scholar citation exceeding 1,900 (Hirsch Index 22). Through recent YSEP Program, Dr Deo will collaborate with Chinese researchers in artificial intelligence-based energy systems.