Sean is interested in problems associated with learning and updating models in a “Big Data” setting. Many real‐world problems involve underlying models that evolve over time; data-mining and machine learning are essential tools to reformulate models of evolving decision problems. The advancement of Neural Networks is leading to new research opportunities for real world problem solving. Neural Networks are computational networks that attempt to simulate the decision process carried out by the human brain, in order to derive meaning from complicated data.
Sean’s research focuses on data from the electricity market. Electricity is generated, transported, traded, delivered and consumed in real time, making it an excellent case study for a constantly evolving big data problem. Sean is focusing on the approaches used in the short term forecasting of the multiple time series components (demand, wind, price forecasts) for both the Irish and British energy markets, looking at optimising the current statistical and machine learning models, while also investigating whether Artificial Neural Networks (ANN) and Deep Belief Networks (DBN) could be effectively adapted to predict the short term forecasts. Sean’s challenge is to find whether using Neural Networks is an efficient way for providing forecasts in the energy markets.