Project: Forecasting water demand using smart metering data
Supervisors: Professors Zoran Kapelan, Slobodan Djordjevic and Jan Hofman
Accurate forecasts of demand are a key input to understand future supply-demand balance. While long-term predictions can be used to develop pro-active strategies that will secure water for the future, short-term forecasts assist in the operational and financial management of the system.
A major part of demand forecasting is understanding the underlying processes and identifying the determining factors that drive water consumption. The current research aims to utilise a combination of statistical methods and machine learning techniques in order to identify patterns and establish relationships between water demand and a variety of factors that are suspected to influence it.
In order to achieve this, an extensive dataset comprising of high-resolution consumption data (derived from smart meters), household characteristics, socio-economic factors, and weather variables became available. The methodology adopted is based on a systematic approach that evaluatesthe relationship between water consumption data and explanatory factors for different temporal and spatial scales and aggregations of households (based on household characteristics and socio-economic data), while implementing uncertainty and risk based planning
Maria is currently based in the Centre for Water Systems at the University of Exeter, whilst collaborating closely with Wessex Water. Before joining the WISE Programme in 2015, she graduated from the National Technical University of Athens (NTUA) with a MEng in Civil Engineering. Whilst at university, she completed a year-long internship as a research assistant at the Berlin Centre of Competence for Water (Kompetenz Zentrum Wasser Berlin) in Germany, where she worked on project SEMA (SEwer deterioration Model for Asset Management strategy). Following her return to Greece, she also completed a four month internship as a data analyst for a Greek start-up company. Her research interests lie in the intersection between civil and software engineering and include water demand pattern recognition and forecasting, statistical analysis and machine learning.
WISE 2017 Summer School Presentation: Maria_Xenochristou_Jun17
June 2017 Poster: Maria Xenochristou Jun17