The energy transition represents one of the most significant challenges of our time, demanding innovative solutions to decarbonize, digitalize, and decentralize energy systems.
Artificial intelligence has emerged as a transformative technology with the potential to meet these challenges and drive the transition forward. Yet, adoption of AI in utility operation and processes requires overcoming significant barriers, which calls for a different approach to innovation and collaboration.
Following examples from many other industries like telecommunications, finance, automotive and health care, the energy industry needs to leverage open source strategically to innovate, deploy AI with speed and scale, and unlock its potential for the energy transition. And there are a few guiding principles for doing so most effectively.
Enabling scale at the AI-energy nexus
Collaboration through open source will enable AI deployment at scale in energy systems.
Unlike the traditional approach of consortia, non-disclosure agreements, and closed technologies, open source collaboration provides a framework for building digital AI solutions that are adaptable and scalable. It allows stakeholders to develop technologies collaboratively —- and retain at the same time the flexibility to customize these solutions for their unique requirements. This is a more sustainable business model for technology providers.
The development of AI for energy systems requires expertise in power systems, data science, and regulatory compliance, as well as access to vast datasets and computational resources. No single stakeholder can tackle these challenges alone. Through open source, stakeholders can co-develop shared technologies, reducing duplication of effort and accelerating the pace of innovation.
This accelerated innovation is exemplified by the release this week of the open source DeepSeek model that builds on other open efforts while introducing dramatic performance and efficiency improvements.
However, for the mission-critical energy industry, there are even more important benefits. Open source collaboration allows all stakeholders — utilities, system operators, vendors, research laboratories, academic institutions, regulators, and policymakers — to pool their collective knowledge and resources while addressing critical concerns such as trust, transparency, compliance, explainability, and ethics.
Trust, transparency, and sovereignty
In critical sectors like energy, trust, transparency, and sovereignty are essential for the adoption of transformative technologies like AI. Open source directly addresses these pillars, enabling stakeholders to build systems that are not only functional but also accountable and adaptable.
- Trust: Trust is a prerequisite for deploying AI in energy systems, where decisions impact public safety, economic stability, and the environment. Open source fosters trust by allowing diverse stakeholders to scrutinize and contribute to the development of AI tools. This collaborative approach ensures that vulnerabilities are identified and addressed collectively, enhancing system reliability and resilience. For real-world deployment, this shared scrutiny can prevent issues ranging from algorithmic biases to cybersecurity threats.
- Transparency: Proprietary AI systems often function as “black boxes,” making it difficult for users to understand or verify their decision-making processes. Open source breaks down these barriers by providing full access to the underlying algorithms and data flows. This transparency enables stakeholders to audit and validate AI tools, ensuring they align with regulatory standards and ethical guidelines. Transparency also empowers organizations to ensure compliance with recent regulations like Europe’s AI Act, which prioritizes explainability and accountability in high-risk systems.
- Sovereignty and control: For utilities and operators managing sensitive infrastructure, retaining control over technological choices and decision-making processes is critical. Open source empowers organizations to customize AI tools to meet their specific needs, avoiding dependence on closed solutions. It ensures that accountability and governance remain firmly in the control of critical infrastructure operators while allowing them to leverage world leading expertise and global collaboration.
Preparing organizations for AI
To fully realize the potential of AI, energy organizations must prepare themselves for the integration of this transformative technology.
At the heart of AI readiness lies robust data governance. Energy organizations must ensure that data is not only high-quality but also accessible and secure. Open source platforms provide tools and frameworks for managing data flows, ensuring compliance with privacy and cybersecurity regulations. Properly managed data flows facilitate innovation while protecting sensitive information.
In actually training the AI, both digital twins and open datasets are powerful tools.
Digital twins are virtual replicas of physical systems, and allow organizations to simulate real-world scenarios. The twins can enable predictive maintenance, operational optimization, and strategic planning — and open source platforms make these tools accessible to all stakeholders.
Access to realistic and comprehensive datasets is a key challenge for training AI systems, especially in an energy system that is evolving so quickly. Open source initiatives are developing datasets that simulate real-world energy scenarios; for instance, the OpenSynth project is building a community for holders of raw smart meter (i.e. demand) data to generate and share synthetic data and models that can be used by researchers, industry innovators and policy-makers. It was contributed by the Centre for Net Zero, a research center affiliated with Octopus Energy in the United Kingdom, based on their own need to facilitate collaboration with third parties and other data holders across the world.
These datasets enable organizations to train AI tools effectively while maintaining data privacy and security. Synthetic data generation also plays a role, bridging gaps where real data may be too sensitive or unavailable.
Becoming AI-ready requires more than technological investments, though — it demands a cultural shift towards innovation and collaboration. Open source encourages this mindset by fostering partnerships across sectors and disciplines. By participating in open source communities, organizations can tap into a global ecosystem of expertise and innovation. Staff training in AI literacy and cross-disciplinary collaboration is essential to fully harness this potential.
The work underway already
Collaboration is already happening around open source AI solutions for energy systems. Stakeholders from utilities, vendors and academia worked together through a Special Interest Group at LF Energy to identify key use cases and challenges and start establishing projects and initiatives to tackle them. They landed on seven:
- Forecasting: Open source forecasting tools such as OpenSTEF, which originated at Alliander, improve short-term load predictions, enabling better grid management and integration of renewable resources.
- Accelerated simulation and optimization: Leveraging AI in simulation and optimization tools allows for faster, more accurate decision-making in operations, especially under unpredictable conditions.
- Asset management: AI-enhanced predictive analytics optimize maintenance schedules, reducing failures and extending the lifespan of critical infrastructure.
- Long-term planning: AI tools support long-term planning by simulating infrastructure investments under various climate and market scenarios.
- AI assistants: These tools enhance operator decision-making, particularly in real-time grid management under high uncertainty.
- Grid-interactive smart communities: AI-driven systems enable decentralized energy management, promoting flexibility and efficiency at the grid edge.
Other initiatives address foundational challenges, such as creating digital twin platforms, open synthetic datasets, and foundational models tailored for energy systems. For example, LF Energy’s GridFM project, which was originally created through a partnership between IBM and Hydro-Quebec, exemplifies the potential of large foundational AI models to streamline grid operations and planning.
The energy sector is at a crossroads, with the integration of AI representing a critical step toward achieving global decarbonization and sustainability goals. However, the challenges of deploying AI at scale cannot be addressed in isolation. Open source collaboration offers a path forward, enabling stakeholders to pool resources, share expertise, and co-develop solutions that benefit the entire sector.
Alexandre Parisot leads technical activities and collaboration around AI and energy systems at LF Energy, which is the energy arm of the Linux Foundation. The opinions represented in this contributed article are solely those of the author, and do not reflect the views of Latitude Media or any of its staff.
