Sun, sensors and silicon: How AI is revolutionizing solar farms

With artificial intelligence (AI) dominating the news over the past two years, a new headline is emerging: the pressure these technologies place on our energy systems and grids. The data centres that train and operate models require massive amounts of energy. The International Energy Agency (IEA) forecasts that this demand will double by 2026, requiring roughly the same amount of electricity as the whole of Japan.

Despite the goals of many tech companies to cut greenhouse gas emissions and achieve carbon neutrality, for some, increased AI demand in data centres has caused emissions to grow.

But AI itself might offer a solution. Advancements in renewable energy, paired with AI, could sustainably meet the increased energy demand. One promising path is integrating AI into the growing market of solar energy systems that offer clean and affordable energy to grid systems. According to the IEA, power sector investment in solar photovoltaic (PV) technology is projected to exceed $500 billion in 2024, surpassing all other generation sources combined.

Furthermore, Indigo Advisory identified over 50 potential applications of AI in energy, with over 100 vendors already integrating AI into their products, driving a $13 billion investment in the sector.

Harnessing AI in solar energy applications presents a unique opportunity — and it can help overcome certain challenges facing solar energy. For example, solar panels’ reliance on the sun shining makes them a less reliable source of energy than nuclear or gas. Extreme weather events, such as heatwaves or sandstorms, which are becoming more frequent, can also interrupt solar energy supply, while grid constraints limit the potential of solar projects.

These challenges to the solar industry are very real, but AI can help to overcome them meaningfully. Here are just a few examples.

AI is emerging as a game-changer in weather and solar generation forecasting. AI algorithms analyze meteorological data to generate precise forecasts, maximizing solar output and improving grid management. This allows solar operators to better plan and mitigate the impact of intermittent energy supply. Furthermore, cloud-imaging technology and sky cameras enable capturing real-time data on cloud movement and atmospheric conditions, enhancing the precision of solar power predictions.

Beyond forecasting, AI is transforming the predictive maintenance of solar panels. Cutting-edge machine learning models continuously monitor and analyze data from solar installations, reducing downtime and maintenance costs while extending the lifespan of solar equipment. To swiftly identify anomalies, these AI systems monitor temperatures, irradiance, orientation, tilt angle, humidity, rainfall, dirt accumulation, power output, inverter efficiency, and operational loads. Predictive maintenance can increase productivity by 25%, reduce breakdowns by 70% and lower maintenance costs by 25%.

Data-driven analytics tools such as Univers Solar Advanced Analytics can play a role in providing data-driven recommendations for corrective actions for PV projects. Analyzing all corrective actions throughout 2023 for a fleet of over 300 sites, 28,000 devices and more than 11GW, Univers found distinct seasonal patterns categories of corrective actions, including inverters, trackers, DC health, sensors, grid and data availability.

AI also improves the speed and efficiency of reporting and data querying through advanced chat interfaces. Energy management platforms are integrating human-in-the-loop intelligence and natural language processing (NLP) into their systems.

Sophisticated chatbots with NLP capabilities simplify technical complexities, making data more accessible and speeding up the process of obtaining critical information. Solar operators and consumers can quickly access information on energy output, system health and maintenance schedules, streamlining operations and ensuring proactive management of solar assets.

Integrating AI with battery storage systems revolutionizes the timing of energy storage and release, allowing providers to adapt to real-time market conditions and fluctuating energy demands. With the oversupply of energy from renewables during certain times of the year expected to increase, these battery storage systems can be collocated with solar installations, synergistically enhancing energy use. This dynamic trading capability enhances stored energy utilisation, maximizes profitability and ensures a balanced supply and demand in the energy market.

AI undoubtedly has the potential to impact the renewable energy sector significantly, but its broader implementation in the industry is not without challenges.

A major risk associated with AI is the increased vulnerability of critical energy infrastructure to cyberattacks. A notable example is the 2015 power grid hack, believed to have originated from a foreign actor, which left 230,000 Ukrainians without power for six hours. This incident is particularly concerning, given the rising geopolitical tensions worldwide.

Another major challenge is that energy data often comes from diverse sources and in varying formats. Inaccurate or incomplete data leads to erroneous insights and decisions, undermining the effectiveness of AI applications. AI compatibility with Internet of Things (IoT) data is essential for addressing challenges in solar energy. IoT devices collect real-time data on production, consumption and environmental conditions. Integrating this data with AI enhances energy management analytics, providing accurate insights and reliable decision-making for energy providers.

Furthermore, traditional language models like LLMs are primarily trained on textual data, but sensor data from energy systems often comes in different formats (e.g., numerical, time-series), requiring the development of new models or adapting existing ones, such as Archetype AI, to ensure compatibility and accuracy in energy applications.

As the adoption of AI accelerates, so does the need for more computing and, as a result, the need for more energy. Solar energy presents a clean, predictable and affordable solution that, paired with AI, could be one answer to energy shortages. This combination of technologies provides much-needed solutions that facilitate the growth of AI while reducing greenhouse gas emissions.

Embracing AI in the renewable energy sector, despite its challenges, promises a future where clean energy and AI synergize to meet our energy needs and climate goals efficiently and sustainably.