The SUPERNOVA project is set to revolutionise the solar photovoltaic (PV) sector by leveraging artificial intelligence (AI) to enhance efficiency, data management, and operational maintenance practices across the entire value chain. This ambitious initiative, comprising 20 diverse partners, aims to break down silos and foster collaboration among manufacturers, project developers, independent power producers, operations and maintenance (O&M) providers, and research facilities. The project’s overarching objective is to answer two critical questions: how can each stakeholder’s work be positively impacted by changes in previous steps of the value chain, and how can they, in turn, positively impact the next phase?
SUPERNOVA’s seven specific goals are designed to address these questions comprehensively. The first objective is to enhance the grid-friendliness of O&M during the design of PV plants. This involves considering not only yield maximisation but also optimising for various O&M procedures, such as manoeuvring unmanned vehicles or designing structures to withstand harsh weather events. “When planning and designing a PV plant, we consider not only maximising yield but also optimising for various O&M procedures,” a SUPERNOVA spokesperson explained.
The second objective focuses on leveraging robotic solutions, including drones and field robotics, to reduce costs and monitor PV modules for potential failures. The third objective involves improving sensing solutions for data collection, aiming for high spatial and temporal granularity. This includes developing smart modules and string-level Internet of Things (IoT) devices to assist in monitoring and detecting failures. The sheer volume of data generated from these advancements necessitates effective data management, the fourth objective. AI will play a crucial role in simplifying access to and analysis of all the data collaboratively, leading to what SUPERNOVA refers to as an ‘insight explosion.’
This increased insight will facilitate better operational maintenance and enhance considerations regarding the end-of-life status of PV components. The fifth objective aims to increase the profitability of PV systems by rethinking operational maintenance and grid-friendliness strategies. This includes developing new business models to monetise data and improve energy trading practices. The sixth objective is to create confidence and business value for sharing data. To achieve this, SUPERNOVA is developing models to overcome privacy issues and establishing best practices for data sharing and monetisation. A key element is the creation of an energy data space tailored for the PV sector, allowing for customised sharing in accordance with the data owner’s requirements.
AI can significantly enhance quality assurance processes within the solar PV sector by leveraging the vast amount of data generated. This information greatly improves our understanding of the operational health of PV plants, enabling us to analyse issues in relation to specific variables, such as materials and climatic conditions. However, the analysis of this data is often complicated, as information from different sources can be confusing, and the sheer amount of data makes the process time-consuming and labour-intensive. AI offers an opportunity to extract hidden insights from this large volume of data. Instead of analysing different data segments individually, AI can help effectively fuse data from various sources and provide whole new insights, the ‘insight explosion’ mentioned before. It is crucial to distinguish between two AI concepts: discriminative AI and generative AI. Discriminative AI has been utilised in PV research for many years, while generative AI, which is based on large language models like GPT, has gained significant popularity in recent years and is now widely applied. Both approaches are essential for achieving this insight explosion, but it is vital to identify the role each approach should take. For instance, a well-trained discriminative approach (the traditional machine learning method) can perform simple defect detection tasks more accurately and efficiently. On the other hand, generative AI is more suited for organising data from various sources, determining the best tools for specific data analysis, and establishing connections between the information obtained. At SUPERNOVA, we are actively integrating both AI approaches. One example of this integration is the development of AI agents based on generative AI, which utilise traditional models trained with data to retrieve and elaborate on relevant information. These agents can be applied in specific workflows, such as O&M.
SolarPower Europe plays a vital role in the SUPERNOVA project, leveraging its expertise to communicate and disseminate information about SUPERNOVA’s work. As a business member organisation, SolarPower Europe unites hundreds of stakeholders across the entire value chain, maintaining direct communication with policymakers and possessing unique knowledge about existing and upcoming policies and regulations. One of the key tasks of the organisation is defining cybersecurity guidelines and evaluating the impact of decisions along the value chain. Our engagement with a diverse array of stakeholders is invaluable, facilitating connections not only within the consortium but also with other partners. Additionally, SolarPower Europe organises important initiatives that are firmly integrated into the project. One such initiative is the Solar Quality Summit, which serves as a central meeting point for stakeholders. At this