Data is as valuable to the low carbon energy transition in the 21st century as oil and coal were to the 20th century energy system.
Data from a huge array of sources – exploration, generation, transportation, distribution and consumption – is one of the main drivers towards the transition to a smart, sustainable and secure energy system.
However, data is only helpful when it is refined to allow us to derive valuable information. The emergence of big data – produced by the ever-growing proliferation of sensors that passively capture all kinds of data at multiple levels, combined with real-time data coming from markets, weather stations and even transport networks – means that the industry is faced with the challenge of how to handle and utilise data in three ways: volume, velocity and variety.
Businesses are increasingly struggling with how to organise, store and extract value from big data, which is where data science and machine learning come in. These knowledge disciplines can help not only extract valuable insights from big data that are crucial to cutting operational costs, optimising investments and reducing risks in the energy industry, but also helping develop smart energy systems that are indispensable to the UK’s transition to a low carbon economy.
The Energy Systems Catapult is utilising state-of-the-art techniques from data science and machine learning across a range of programmes. For example, our Smart Systems and Heat programme, which aims to develop future-proof, economic, smart local heating systems for the UK, is using data science to understand how energy is generated, transmitted and used by consumers, and machine learning to solve existing challenges faced by the industry. We are also developing intelligent energy systems with a holistic view to ensure that modern energy infrastructure is evolving to take account of the energy needs of the business and consumers.