ENERGY

Increasing efficient energy creation and use

Challenge identifier: SC3-2018

Background  

There are several challenges for the EU energy market as competition becomes increasingly intense at the global level. Pressures arise from traditional resources growing scarce and the need for greater environmental protection combined with an ever growing demand for energy, set to increase by a third by 2040. Technological developments however have meant that our ability to produce new types of energy is fast improving, and is currently leading to the most diversified fuel mix ever seen – with unconventional oil and gas, or renewables such as wind or solar energy. This diversified market presents challenges but also a host of opportunities demanding more holistic and comprehensive approaches to encourage efficient energy use and the continued move towards lower carbon energy sources.

At the same time a large number of data sources are becoming available across the energy sector. The smart home market for example is estimated to be worth approximately $10bn per year globally and is expected to grow rapidly. The massive increase in data availability will enable energy suppliers to improve efficiency up and down the customer value chain from acquisition to service, retention and supply balancing. This means that opportunities exist for cutting edge and innovative data-driven approaches across the range of challenges facing the energy sector.

Description

Energy efficiency is an issue for both renewable and fossil fuel energy. We are particularly interested in solutions that leverage closed and shared data in the following areas:

  • Renewable energy generation and alternative fuels – Wind farms and solar energy facilities will require prediction capabilities to ensure efficiency and predictive maintenance.
  • Support the development of the ‘Smart Grid’ – technology gives the opportunity to react to changing energy needs at a more local level.
  • Network maintenance and optimisation – Artificial intelligence and machine learning could also increase the efficiency of current fossil-fuel based energy systems.
  • Consumer understanding and energy efficiency – in many European countries there has been the roll out of Smart Meter technology to domestic energy customers. On the basis of the data produced customers will need support to reduce their energy consumption and help energy providers.

Data

Examples of data include but are not limited to:

  • Energy supply data, network performance data
  • Energy use data both domestic and commercial uses
  • Geographic and environmental data – mapping data, meteorological data etc.

Expected outcomes

Examples of outcomes include but are not limited to:

  • New apps and services
  • New prediction algorithms
  • New intermediary technologies to integrate data sources
  • New tools and business processes to help decision making, including those making algorithms more transparent and accountable, registries and distributed ledger applications
  • New forms of hardware
  • Applications must include details on how these outcomes will be tested and evaluated during the six-months acceleration programme.

Expected impacts

Participants will need to demonstrate how their solution:

  • Increases energy efficiency and sustainability throughout the value chain;
  • Combats climate change;
  • Support decision makers by providing improved evidence bases

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement 732506. Unless otherwise indicated, all materials created by the Data Pitch consortium are licensed under CC-BY 4.0