Phil Grünewald FICE, PhD

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

He is a Supernumerary Research Fellow and 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 co-developed the world's first EUV (13nm!) lithography tool for Intel, as a Marie Curie Fellow, and laser processes for the photovoltaic industry. He also cycled round the world.

• Low carbon energy systems
• System and demand flexibility
• Household energy data
• Data privacy and synthetic data
• Socio-demographic and diary data
• Machine learning and clustering approaches to explain energy demand
• Whole system solutions

EDOL

Energy Demand Observatory and Laboratory. EPSRC project collecting detailed longitudinal data on household energy use and explanatory socio-technical variables

JedAI

Justice, Energy, Demand flexibility and AI for Sustainability seeks to help household take advantage of new flexible use patterns and avoid disadvantaging those less able 

SENSE

Data for good. Providing a range of data sources for wider use in research and development.

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

EDGE³

The Energy Data Group with focus on Environment, Equity and Ethics