4 Apr

Harnessing Data to Support the Energy Transition: How Mesh-AI & TotalEnergies in the UK are making Data a Strategic Asset

Deepak Ramchandani Vensi

The energy sector is going through a significant transformation as the world continues to evolve and demand more sustainable, cleaner, renewable energy sources. Several factors including environmental concerns, declining costs of renewable energy and changing consumer preferences are driving this transition.

With companies facing rising costs amidst an ever-changing market landscape, the energy sector is under immense pressure to innovate and keep up with modern demands. All this while striving to reach ambitious net-zero targets and transition towards an integrated multi-energy ecosystem. However, with this complex transition comes numerous opportunities to utilise data and artificial intelligence (AI) to transform the entire energy supply chain - starting from upstream activities, related to exploration and production, through to the downstream process of supply and grid management.

Across the sector, there is an appetite to digitise and utilise data to its full potential. However, getting there will be costly in an industry where many players rely on traditional infrastructure for critical services and with operating models not ready for change. Only a radical overhaul that focuses on cultural change and looks to make data a strategic asset within the organisation can address these issues head-on.

As part of this article, we deep dive into some of the opportunities for the industry to utilise data and AI and how TotalEnergies and Mesh-AI are working together to make data a strategic asset.

Opportunities to Transform with Data and AI

1) Accelerating the transition to clean energy

Enhance production and improve asset maintenance: data from production performance and asset operations can be used to better understand, analyse and increase the efficiency of energy production. In this scenario, companies can create Digital Twins (a virtual replica of a physical object or system) of their offshore platforms or wind/solar farms. These can help better understand the state of their assets in real-time and create AI models to predict when parts will fail, identify areas for optimisation, and help control the production process.

Optimising exploration: given the costly nature of exploration activities and the need to significantly increase renewable energy production, AI algorithms can be used to analyse vast amounts of data on weather patterns, wind speeds, and solar radiation levels to help energy companies make more informed decisions about where to locate and build renewable energy infrastructure. Just as AI greatly helped optimise subsurface data analysis for oil and gas exploration, it can be used to identify the most suitable sites for wind farms or solar panels based on local weather conditions and energy demands. 

2) Increasing the resilience in the energy supply chain

Better understand consumer patterns: by analysing data from sources such as smart meters, energy companies can better understand how their customers are consuming energy by identifying patterns and trends in energy use and enabling real-time monitoring and analysis of energy consumption and production. This information can then be used to develop targeted energy efficiency programmes and to optimise energy generation, distribution and consumption to reduce waste and inefficiencies.

The transition towards a smart grid: A smart grid is an electricity network that uses data, digital and other advanced technologies to monitor and manage the transport of electricity from all generation sources to meet the varying electricity demands of end users. These systems use data and AI technology to monitor and control energy usage, helping to improve the efficiency of the grid and reduce the impact of energy usage on the environment and proactively identify and fix issues before they lead to a breakdown or outage.

Looking at other industries

The adoption of data and AI within the walled gardens of one’s organisation is not going to be enough, however. Energy companies must work with the wider ecosystem to create a collaborative environment for sharing data and encouraging increased digitisation. By combining their knowledge and resources, companies can drive innovation in the energy sector and accelerate the transition. This is where the energy industry can perhaps learn from innovations within the finance sector such as Open Banking and we might then see the term Open Energy gain momentum.

As the energy industry transitions towards more renewable sources, it can learn from other industries that have already adopted data and AI. The mining industry has successfully implemented AI to optimise extraction processes, enabling them to uncover new sources of minerals more cost-effectively and efficiently. In the chemicals sector, companies are turning to AI to improve product quality control and compliance with regulations. In the construction industry, businesses are using predictive analytics to forecast workforce demand more accurately while making more informed decisions regarding project timelines and costs.

Furthermore, the Net Zero Technology Centre, for example, is working on developing and deploying technologies that reduce emissions, unlock the full potential of an integrated energy system, and aims to propel the energy industry towards a digital, automated, decarbonised future. By working together with the broader ecosystem to share data and make use of technology, energy companies can help ensure they are on track towards achieving zero-carbon emissions.

How Mesh-AI and TotalEnergies are evolving its data culture

Mesh-AI is working with TotalEnergies’ upstream business in the UK to evolve its data culture and build a new strategy based on data mesh principles. This modern approach emphasises on federated data governance capabilities, cross-functional data teams based on product thinking and domain-driven accountability. Together, we are ensuring data is trustworthy, discoverable, and accessible for colleagues across the organisation and the broader external partner ecosystem.

The data mesh approach offers TotalEnergies an automated means to discover, manage and improve the availability of data, whilst driving governance and ownership via data products. This approach provides TotalEnergies with the foundations to accelerate insight through designing, building, and running AI-enabled applications to drive significant business value and operational efficiencies.

Building the pathfinder for Data Mesh leveraging Emissions data

We’re bringing this approach to life through TotalEnergies’ Carbon Emissions data. To help the company achieve its emission reduction goals, emissions data needs to be shared across the organisation and with external stakeholders. Mesh-AI and TotalEnergies are creating data products that are catalogued and serve this data to the broader business - enabling better carbon intensity tracking.

Final Thoughts

The sector has a great opportunity to re-think how it operates as part of the energy transition with data and AI at the heart of it. The first step towards this is making sure that every organisation has a good understanding of the data it possesses, can ensure its quality and is able to make that data available for use in analytics or AI. Failure to manage and govern one’s data would lead to any AI initiatives being put on hold until good quality and well-governed data are available for further analysis.

By combining high-quality, easily accessible data, the sector can drive real business value by leveraging machine learning capabilities to optimise operations, strengthen supply chains and reduce carbon emissions. With the right technologies in place, energy companies can reimagine how they operate while meeting environmental goals, providing an invaluable opportunity to lead the way as sustainable businesses of the future.

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