PhD Title - Topics in High Dimensional Energy Forecasting
Email - email@example.com
Start Date - October 2015
Degree - MSc Sustainable Engineering – Offshore Renewable Energy
Energy forecasts are essential for the reliable and economic operation of modern power systems. Power system operators, participants in energy markets, and generator operators require accurate and reliable forecasts of generation on a wide range of spatial and temporal scales. Furthermore, the objective of many decision-makers is to manage the risk introduced by the limited predictability of renewable generation. This demands that power forecast uncertainty be quantified through use of probabilistic forecasts.My research is focused on developing scalable probabilistic methods for predicting wind power at the national spatial scale, using Big Data processing and machine learning techniques. The research aim is to quantify the uncertainty in wind power forecasts for the 100+ utility scale wind farm sites in the UK and importantly the forecast error dependencies in space and time.
Additional Information - Student Member IEEE (Power & Energy Society)