Unlocking the value of data in the energy sector

conceptual big data image
Image: Shutterstock

Correctly utilising data can supercharge a business today, as it is the greatest differentiator in a world where consumers demand extreme personalisation, writes Michael Ewald, director of technology at Contino.

Businesses are reinventing themselves to take advantage of the insights and actions that rich data sets, coupled with machine learning (ML) and artificial intelligence (AI) can provide.

Related article: Why energy companies need cloud-to-cloud data backups

Nowhere is this more apparent than in the Australian energy market, which is going through its own data-lead revolution. From a wholesale view, spot electricity prices which were previously updated every half hour, shifted to a five-minute schedule in 2021 giving power companies an even greater insight into demand at a granular level.

The emergence of AI and ML is also having an impact, allowing energy companies to predict demand and to better understand the maintenance schedules of their equipment.

From a generational perspective, renewables are making inroads with 24 per cent of Australia’s energy needs provided by technologies such as wind, hydro and solar in 2020, up from 19 per cent the previous year.

And consumers aren’t being left out of this revolution. First introduced in 2018 to the financial services sector, the Consumer Data Right is being extended to energy, meaning customers will have access to the information power companies hold. This will drive change in customer service, as well as the introduction of innovative new services and companies, such as data driven Energetech organisations.

Energy and big data

Sophisticated energy companies are investing in their big data capabilities, getting feeds through everything from the generation side to the smart meters in homes. The next step for the energy sector is to apply ML and AI to their operations to increase efficiency and integrate new capabilities.

Historically, the energy sector has relied on onsite software and data centres. The rise of cloud computing has changed this, with cloud providers such as Amazon Web Services (AWS) able to apply their ML and AI smarts to large data sets. At Contino, we are proud to have achieved AWS Energy Competency status, which recognises our expertise in helping businesses in the energy sector transform complex systems and accelerate the transition to a future of sustainable energy.

As cloud-based services have emerged, the sector is increasingly seeing the value in combining off-site data sets with AI and ML to gain new insights into demand, supply, and equipment.

AI and ML also allow energy sector companies to accommodate the rise of supply-side energy generation, such as the power fed into the grid by rooftop solar. Australia continues to lead the world with rooftop solar. Recent figures indicate there are 3 million rooftop solar systems installed across Australia, contributing 7 per cent of the energy going into the national grid.

But technology does not stand still, and the nascent area of blockchain is also coming to energy companies. Predicted blockchain, where data is stored in an immutable distributed ledger, has potential beyond just storing transactions, and could also provide the basis for metering, billing, and clearing processes.

Using data to optimise grid connected batteries

The Australian Government’s Energy Security Board’s 2025 Project is designed to ready the energy sector for the transitions coming, including the rise of renewables, distributed smart energy appliances and the introduction of big batteries, such as those being used in South Australia.

Batteries are capable of feeding energy to the grid during peak demand periods, as well as storing the excess energy generated by renewables during low demand times. It’s estimated there is around 25GWh of stationary battery storage in use around the globe, a figure which will only rise in time.

AI and ML also have a role to play in optimising battery storage, but there are some areas which energy companies need to finesse their operations to make use of these technologies.

First, the data needs to be cleaned and ready for the AI algorithms to process. Cloud access and data storage is generally the best way to clean data and make it suitable for AI and ML.

Second, the AI and ML algorithms tend to have the most impact when they’re modelling events which have occurred many times before. They’re not good at one-off or outlying events. One example is storm-readiness. By analysing data from past storm events and associated outages and equipment damage, AI and ML can help energy companies predict future outcomes from storms.

Related article: Sustainable data can help Australia on net-zero path

Data has the potential to completely transform the energy sector. Incumbents can combat new players, new players can battle existing entities, and consumers are better off when they have access to their data and can readily switch to new providers and services.

By using AI and ML, energy companies can also integrate the power needs of tomorrow, along with new and emerging technologies to offer better services and attain a superior understanding of their businesses.

Previous articleKeeping up with the increasing demand of household solar
Next articleTCS selected as strategic partner by Western Power