Description

Energy systems are important to the future of UK industry and society. Members of the Statistics group of the Durham Mathematics department (Michael Goldstein and Dario Domingo), are part of the multi-university CESI (Centre for Energy Systems Integration) consortium whose aim is to reduce the risks associated with securing an integrated energy system for the UK.

This area involves a considerable degree of uncertainty. One of the major sources of information in assessing such uncertainty is by the analysis of computer simulators based on mathematical models for the various areas of application. There are many approximations and uncertainties involved in constructing such simulators and matching the output of the simulators with actual observational data. For example, to evaluate a typical model requires the specification of a collection of input parameters, whose true values are unknown.

This project is concerned with the analysis of collections of energy system data and the investigation of the relevance and accuracy of computer simulators developed to study this data.

The applications that we will choose between are collected from various energy systems, for example

i) two energy networks (gas and electricity) serving an area in the North East of England are modelled by a system taking daily profiles of energy requests from various sources and outputting such quantities as the cost of running the network, cost of imported energy, CO2 emissions, and so forth;

ii) the records of house or building historical energy consumptions are available which may be used to forecast future energy consumption as well as to provide decision support for retrofitting.

In each case, historical records will be augmented by an ensemble of computer simulator evaluations for the problem.

The project is designed both to explore an interesting and important practical area for statistical analysis and also to introduce some basic and very widely applicable ideas of uncertainty quantification for real world modelling and inference.

Prerequisites

Resources

More details about the CESI project are given at

the home-page for the CESI research programme

A good web-site which is related to the general types of analyses which this project gives an introduction to is

The MUCM Web-site

This is the web-site for the Managing Uncertainty in Complex Models (MUCM) project, another consortium in which we were involved. That project is now completed but there are many interesting links to follow at this site, in particular

Uncertainty quantification reading list