Keywords: Low carbon energy systems, Whole system solutions, System flexibility, Energy system transitions, Demand side flexibility, Smart meter electricity and gas data, Socio-demographic and diary data, Machine learning and clustering approaches to explain energy demand, Electrical Power Group, Renewable energy Machine learning Systems and Sustainability

Biography

Dr Phil Grünewald (FICE) uses interdisciplinary and data driven approaches to understand and household energy demand and its flexibility.

He is an Oxford Martin Fellow and a Tutor at Oriel College. Phil held an EPSRC Fellowship from 2015-21 and was PI of the METER study, which pioneered machine learning approaches to understand household energy use via diary and meter data.

Before joining the Engineering Science Department, Phil was at the School of Geography and the Environment (2013-20), where he led the Oxford Energy Network and the Flexibility theme of the Energy Program in the Environmental Change Institute.

Phil was awarded an interdisciplinary UKERC scholarship for his PhD at Imperial College London on the future role of grid storage.

Prior to academia, Phil developed the world's first lithography tools for Intel, as a Marie Curie Fellow, and laser processes for the photovoltaic industry. He also cycled round the world.

Current Projects

  • Shift-0
    Learning from changes in gas and electricity use patters after heat pump adoption. Funded by MCS Charitable Foundation
  • ReNEW
    Reconfiguring Energy Needs, Equity and Wellbeing. An Oxford Martin Programme observing household's ability to change energy use
  • EDOL
    Energy Demand Observatory and Laboratory. EPSRC project collecting detailed longitudinal data on household energy use and explanatory socio-technical variablestails