Time series modelling and forecasting of disaggregated electricity data

The electricity industry is collecting large volumes of data from various sources. From regional grid demand to individual sensor readings in buildings, there is a wide range of disaggregated data sources requiring new techniques for forecasting and inference. A better understanding of how electricity is being used by consumers has the potential to increase energy efficiency and improve grid planning and management. This thesis presents several novel approaches to understanding these varied data sources. Contributions include advances in hierarchical probabilistic load forecasting; inference and forecasting using smart meter data and building characteristics; and exploratory analysis of building management system data.