Why is artificial intelligence needed to drive the green energy transition?

Today, we see clear trends and momentum towards decarbonization and green energy transition. At the same time, the rise of digital technologies and advanced analytics provides unique opportunities not only for the development of new energy technologies, but also for monitoring progress, predicting performance, integrating systems, ensuring reliability and resilience, and through Optimize products, solutions and services to improve sustainability like never before.

But at the same time, the changing dynamics of the industry have added to its complexity. The web is moving from a centralized model to a decentralized model. Energy producers have multiple OEM (original equipment manufacturer) solutions that must be monitored as a system to ensure uptime and output. Venture capital is increasing and there are many new entrants in the market, disrupting different areas of value creation. Governments, activist investors and communities are increasing pressure for transparency on ESG indicators along the value chain.

Easy access to data among different stakeholders is a key factor in promoting competitiveness while maintaining equitable participation across the entire energy value chain. In the future, markets and infrastructure in different industries will be closely connected. Therefore, safe and reliable data sharing is needed to promote innovation within and between industries.

However, the energy industry has been slow to adopt modern digital technologies and may be at risk due to its critical role as critical infrastructure. We see that the transition to digital is slowed down by poor data quality, inaccurate or missing data, a lack of modern data architecture, and the fact that data is often tight and restricted or hard to find. Optimizing energy systems will require better digital information, data transparency and open standards, while ensuring appropriate security and data protection measures. Cybersecurity is absolutely necessary to build trust, confidence and resilience for grid stability and information flow.

To support these changes, standards and regulations are needed to promote compatibility and interoperability. Digitalize information exchange, streamline product development, accelerate time to market for solutions, and increase transparency and trust.

The role of artificial intelligence in changing the global energy landscape

One thing is certain about the future: the interactions between energy systems will become more complex. Key challenges we face include decarbonization, decentralization, energy storage, waste reduction and smart maintenance. Overcoming these challenges will require creative thinking that goes well beyond the methods traditionally applied to engineering. Artificial intelligence (AI) methods and frameworks will be at the forefront of overcoming these complex challenges.

To successfully meet the huge challenges posed by the energy transition, there is a need to move beyond incremental changes and come up with new transformative innovations that go beyond traditional engineering.

Artificial intelligence is an expert at this job, and this technology is perfectly suited to the vast amounts of data generated by all parts of today’s value chain, as well as the ever-increasing computing resources. For example, machine learning methods allow it to systematically tailor products, solutions and services to meet specific needs. AI-based solutions also greatly help deal with the increasing complexity of energy systems due to decarbonization and decentralization. Additionally, it allows for improved predictions of hardware durability to optimize maintenance cycles and thus reduce waste. By using artificial intelligence, power plants can be more efficient and reliable, reduce emissions, and optimize the use of materials, all of which contribute to greater sustainability. By implementing self-optimization processes in the manufacturing process, delivery times can be optimized, and autonomous operation of power plants can enable greater security and improved grid stability through more efficient power generation.

The importance of “open data” to society

The concept of “open data” has been around for over a decade and has underpinned everything from a plethora of navigation solutions to transparency in government spending , to innovation in emerging applications in the automotive field. When certain data sets enter the "public domain," we see innovation flourish in unexpected ways, driving society forward. That said, it is clear that we must balance the needs of the public interest with companies’ genuine concerns about intellectual property, revenue opportunities, and customer consent and trust.

Why there should be industry standards for all ESG measures

There should absolutely be standards for ESG measures, including scopes 1-3. It is in the public interest to maintain transparency and trust in the data reported, and how it is measured and calculated. Without standards, there are increased burdens and risks to the public interest because information reported by multiple companies is not comparable. This can be seen, for example, in Covid-19 reporting, where countries report statistics in a way that makes country-by-country comparisons difficult without additional work.

The biggest challenge is tracking scope 3, the company’s supply chain. Whether it is packaging, agriculture, manufacturing or other suppliers, attention will continue to turn to this value chain. Introducing science-based standards will give credibility and transparency to these figures while reducing the cost burden on businesses, especially small and medium-sized enterprises.

Financial Investment Accelerates Transformation

From a data perspective, building and maintaining competitiveness in data and artificial intelligence is critical to keeping Europe at the forefront of technology. This process spans early education, academics and reskilling. To achieve this, close collaboration between public agencies and industry is required. This can be driven by co-funding research projects, as well as funding for data science and AI tracking at universities at all education levels.

Venture capital and startup funding are also important to build an ecosystem of startups that will continue to advance areas such as battery storage, AI, additive manufacturing, sensor technology and other technologies critical to digital technology of innovation.

Ensuring a balance between industry and public interest

No one, no company, no government is immune to the impacts of climate change. It is therefore imperative that we all find solutions for the transition to net zero carbon and decarbonization as quickly as possible. Digital technology and artificial intelligence will power future solutions, but industry needs government support to develop standards to simplify the path and transition forward. Governments should work with industry and other stakeholders to develop standards that ensure targets are met without too much burden, or shared avoidance.

We have already seen the success of this approach in the automotive field, for example, with safety-related traffic information (SRTI). However, it is also important to encourage industry to share intellectual property and create opportunities for value.

Positioning the EU as a leader in standards setting

The General Data Protection Regulation (GDPR) was groundbreaking when it was published and has since become a wake-up call for privacy standards. It is often the default standard used by many global companies when managing sensitive customer data around the world, as it provides the ability to ensure compliance while reducing application and system complexity.

In a similar way, the EU can take a leadership role in developing data and digital standards to drive interoperability and support the energy transition. To complement this, a European standardized framework on the development and implementation of AI workflows is needed.

Learn from other industries

In addition to some of the examples above, there are many examples around us. Our ability to move money easily between countries, the rise of internet standards and e-commerce, and container standards that increase transparency in logistics. There are usually some examples of what other industries are doing well that you can learn from and adapt. It’s important to understand what can be learned from this, and how can we accelerate the pace by building on models that have been proven to work, with policy, investment, standards and technology as core pillars?

The above is the detailed content of Why is artificial intelligence needed to drive the green energy transition?. For more information, please follow other related articles on the PHP Chinese website!