ARTIFICIAL INTELLIGENCE FOR POWER LOAD AND RENEWABLE ENERGY FORECASTING IN ELECTRICITY GRIDS
Short-term forecasts of (a) power load and (b) renewable energy supply, are crucial for decarbonising electricity grids: without these forecasts, high-carbon baseload generators must be kept running.
The scientific challenge is to achieve accurate and reliable forecasts, in the face of changeable energy demand patterns and external covariates (weather, public events, etc). Deep learning has been shown to perform very well on power-load forecasting and achieves promising results on renewable-energy forecasting (Wang et al., 2019). This PhD plan sets out to develop deep learning algorithms that realize forecasts that are both accurate and reliable, with a flexibility to adapt to local conditions.
PhD promoters: Dr. Dan Stowell and Dr. Ciçek Güven