Localised high resolution forecasting for energy demand based on smart meter data

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This is a PhD in Energy and Artificial Intelligence. Using smart meter data provided by the partner utility company, this PhD will investigate the applicability of bottom-up forecasting models for energy demand trained on historical data from individual smart meters.This PhD will investigate, using smart meter data provided by the partner utility, how bottom-up forecasting models can be used to predict energy demand at a number of scales. These models will be trained on historical data from individual smart meters, and the forecasts will be aggregated at a local/regional level and compared with the accuracy of top down forecasts to assess what benefits exist and what level they occur.Particular avenues of exploration could be:- Spatial information allowing exploration of scenarios such as the impact of Locational Marginal Pricing (nodal pricing), a policy implemented in the United States and much discussed in the UK, on a utility based on its current portfolio of customers and the impact of the spatial distribution of its customers on profitability.- the potential for balancing portfolios of customers, locally or within the suppliers base, and identifying locations of possible network constraints and substations likely to reach capacity with the uptake of electric vehicles.- Design of an optimal trading agent for the hedging and purchasing of electricity given the portfolio of smart meter information is a likely final application of the research that would bring together a number of the individual strands of the project.Methods:As a starting point, state-of-the-art ensembling methods will be tested as a benchmark before exploration of the potential for deep learning methods in particular Long-Short-Term-Memory Neural Networks to improve results. Reinforcement learning and agent-based models will also be explored throughout the course of the project.