Artificial Intelligence Methods For Electricity Market Imbalance Prediction

The core objective of the PhD is to identify drivers of UK electrical imbalance, create forecasting tools to better anticipate them, and then form a probabilistic price forecasting model for the balancing market.

Novel artificial intelligence methods, specifically Bayesian deep learning, will be employed for the forecast model development. The forecasts will be carried out across varying spatial and temporal horizons to identify the optimum forecast resolution which provides the most value to system operators. This will require the use of multiple existing and new datasets sourced from across the UK and Europe.


The proposed PhD topic involved the development of Artificial Intelligence approaches that can make use of diverse and disparate data relevant to electricity markets such as weather forecasts, renewable generation production, historical demand and many other factors to develop month, week and day ahead forecasts of the imbalance volume/price in the market per half hourly period. The research will also involve estimating the level of uncertainty around these predictions and developing an understanding of electricity market structure and interpreting findings to promote better market design. In your PhD you will be expected to develop expertise in Artificial intelligence methods from reinforcement learning and neural networks to Bayesian deep learning. The project is highly interdisciplinary one requiring an understanding of power systems, market design and advanced artificial intelligence methods and the ideal candidate is expected to be comfortable and interested in working with colleagues across these domains.