PhD Scholarship in Analysis of High Frequency Energy Consumption Data in Denmark | Technical University of Denmark (DTU)

Systems Analysis Division, Department of Management Engineering, Technical University of Denmark invites applications for a 3-year PhD position in the analysis of high frequency ('smart meter') energy consumption data. The PhD student will contribute to the research of the strategic research center IT-Intelligent Energy Systems in Cities (CITIES). CITIES is funded with a budget of €10 million from The Innovation Fund Denmark and a wide range of industrial and academic partners. Research activities are initiated in CITIES in order to support the Danish target of a 100% renewable energy system by 2050 with solutions for future smart cities. The goal of CITIES is to provide research results which will lead to IT-solutions for future smart cities that spread across the dimensions of time, space and detail.

Responsibilities and tasks

The PhD student will apply and develop statistical, econometric and related methods to analyze high frequency (smart meter) data of electricity and heat, which can be used by businesses and individuals to decrease energy usage, shift load and save money. The work includes demand forecasting, computing consumption profiles, analyzing socio-economic and behavioral data, identifying outliers, and contributing to thermo-dynamic building modelling.

The main tasks will be to:

• Analyse high-frequency energy consumption data related to the CITIES project
• Create and apply models including energy consumption, behavioural, socio-economic and physical variables.
• Participate in project-related meetings and management tasks
• Publish research papers in international journals and conference proceedings
• Assist in the teaching and supervision of students

Qualifications:

Candidates should have a master's degree in economics, statistics, engineering or a similar degree with an academic level equivalent to the master's degree in engineering. The applicant should furthermore be familiar with time series modelling of high frequency data or related econometric and statistical methods.