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How can we best segment customer energy use profiles?

Background

BEIS has committed to ending the UK’s contribution to global warming by achieving net zero
greenhouse gas emissions by 2050. Our work towards becoming a leader in green
technologies and clean energy will drive economic growth, all whilst accelerating global climate
action through strong international leadership. To achieve this, we need to better understand
the following research questions:

Next steps

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Source

This question was published as part of the set of ARIs in this document:

Beis areas research interest interim update 2020

Related UKRI funded projects

  • Highly Personalised Energy Advice

    The project aims to scale and deliver to a cloud environment a highly advanced analytical software solution, which will analyse data from Smart Meters & Customer Access Devices (CADs) and detect up to 12 appliances f...

    Funded by: Innovate UK

    Lead research organisation: ONZO LIMITED

    Why might this be relevant?

    The project aims to segment customer energy use profiles by analyzing data from Smart Meters & Customer Access Devices (CADs) and providing personalized energy insight & actionable advice.

  • University of Reading and Optimal Monitoring Ltd

    To target a market opportunity for small and medium-sized organisations without dedicated energy managers by developing a system capable of identifying unwanted utility usage and proposing solutions to this exceptional c...

    Funded by: Innovate UK

    Lead research organisation: UNIVERSITY OF READING

    Why might this be relevant?

    The project targets small and medium-sized organizations without dedicated energy managers and aims to identify unwanted utility usage and propose solutions for cost and environmental savings.

  • Plan capture of a Domestic Consumption Dataset to inform Smart Demand

    Today's electricity grid models capture only the macro-level. Planning Smart Demand requires a detailed bottom-up model of individual household appliance consumption, a model which does not exist today. Government and th...

    Funded by: Innovate UK

    Lead research organisation: ALERTME.COM LIMITED

  • OFfSET - Optimised Forecasting for Switching Energy Tariffs

    "The Optimising & Forecasting for Switching Energy Tariffs ""OFfSET"" project brings Samsung's UK-based Research team together with energy market disruptors Labrador and home energy managemen...

    Funded by: Innovate UK

    Lead research organisation: SAMSUNG ELECTRONICS (UK) LIMITED

  • University College London And PassivSystems Limited

    To develop intelligent recommender systems for improved home energy management, using consumer profiles and energy consumption data to automatically recommend and optimise services. To embed behaviour change and iterativ...

    Funded by: Innovate UK

    Lead research organisation: UNIVERSITY COLLEGE LONDON

  • Engaging Household Energy Savings and Demand Response Opportunities

    Smart meters are being rolled out widely in GB and internationally – including many developing countries where smart meters are being used to combat prevalent electricity theft and losses. With smart meter data becomin...

    Funded by: Innovate UK

    Lead research organisation: ELEMENT ENERGY LIMITED

  • Overcoming bureaucratic roadblocks with AI for maximum efficiency in energy management

    Are you tired of dealing with complicated energy bills? Are you frustrated with the bureaucratic energy providers? Do you want to pay less and reduce your energy usage but don't know how? We hear you, and we've got you c...

    Funded by: Innovate UK

    Lead research organisation: PARALLAXIS TECHNOLOGIES LTD

  • Residential Electricity Demand: Peaks, Sequences of Activities and Markov chains (REDPeAk)

    Peak electricity demand is becoming an increasingly significant problem for UK networks as it causes imbalances between demand and supply with negative impacts on system costs and the environment. The residential sector ...

    Funded by: EPSRC

    Lead research organisation: University of Reading

  • Responsive Algorithmic Enterprise (RAE)

    Demand side response is a method whereby financial incentives are used to encourage customers to lower electricity use at peak times. This enables load, voltage, thermal, balance and other constraints on the electricity ...

    Funded by: EPSRC

    Lead research organisation: University of Reading

  • Digital Agent Networking for Customer Energy Reduction (DANCER)

    Long-term energy consumption reduction can be achieved more readily through sensible cooperation between end users and technological advancements. Monitoring energy use within buildings requires clear and reliable method...

    Funded by: EPSRC

    Lead research organisation: University of Essex