Time series modelling and forecasting of disaggregated electricity data
thesisposted on 18.07.2020 by CAMERON HAMILTON ROACH
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
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.