Introduction:
The solar industry has always prided itself on being at the forefront of modern technology. Positioned as an energy market disruptor, alongside other renewable generation technologies and energy storage technologies, solar has helped to reshape the way we think about power generation. But the disruptor faces a disruptor of its own, Artificial Intelligence (AI) and its subset Machine Learning (ML). These technologies have the potential to revolutionise the industry as it is today.
Operators in these markets are presented with the choice: either embrace this technology and accelerate performance or risk falling behind as the rest of the industry moves forward.
Traditionally, “asset optimisation” in solar has meant revamping or repowering, but what if the greatest optimisation isn’t hardware at all? What if AI is cheaper, faster and delivers greater gains?
The potential impact on solar optimisation can be grouped into three core areas: boosting efficiency, deepening insight, and enabling intelligent control.
Efficiency
There’s no getting away from it, AI outperforms human capability when processing and interpreting large data sets. Well implemented Machine Learning tools can be used to detect anomalies before they become faults, allowing your operations teams to schedule intervention proactively. In some cases, projects deploying AI-driven predictive maintenance report up to 70% reduction in downtime and 30% decrease in operational costs.
By pairing ML tools with experienced analysts, you free your teams from the laborious work of data processing. Instead of spending hours combing through spreadsheets, analysts can focus on higher value interpretation and strategic decision making, accelerating the path from data to action.
AI planning tools can also automate work allocation, ensuring the right technicians with the right skills are dispatched to the right tasks. The result is a cost-effective operations model that is more efficient.
Improved Data Insight
AI excels at identifying the smallest inconsistencies in data and emerging performance trends that could be missed by conventional monitoring systems – following this you can set your technical experts and analysts to the task of diagnosis. Keep human intelligence focused where it offers the most value and give the grunt work to AI.
AI can synchronise and interpret vast, multi threaded datasets in real time, across every inverter, every string, every asset in your portfolio. It can integrate meteorological data, environmental conditions, site specific performance metrics, and industry wide benchmarks, cross referencing them continuously to detect issues before they escalate. Working at a scale that even the most capable and experienced analytics experts would struggle to match.
This ability to combine large datasets, either to facilitate more valuable analyst insight or to forecast energy production is a key area in which AI can help you unlock the vast potential of data that your assets are already collecting.
The production of solar energy is made more efficient and the performance of solar assets is optimised through the integration of cutting-edge technology like pvAPM (PhotoVoltaic Asset Performance Management) and technical consultancy services. As a result, operational efficiency is increased, asset performance is improved, and ultimately greenhouse gas emissions are decreased.
Intelligent control systems
Today’s solar plants already rely on automated control loops for core functions such as MPPT, inverter protection, and basic grid support responses. But these systems are fundamentally rule based: they react to predefined thresholds and follow fixed logic. They don’t learn, they don’t adapt, and they don’t optimise beyond the scenarios they were programmed for. AI changes that paradigm, it gives us the ability to have automated control systems with intelligent action.
AI enabled control systems can make intelligent, real-time decisions: adjusting inverter behaviour, optimising tracking panel tilt based on weather and sun position or managing charge/discharge cycles for storage paired assets. These systems can meet advanced, instantaneous and complicated grid requirements, a frequent sticking point for traditional controls.
This AI enhanced control compounds the efficiency benefits discussed earlier. It reduces the operational costs, enhances asset protection, and ensures optimal performance in changing conditions. Intelligent control systems can use the deeper data insights to shutdown key elements before they suffer catastrophic damage.
Conclusion:
It is no longer a question of if you should implement AI enhanced tools on your assets, but how quickly you can do it. AI and ML tools can directly address the limitations of traditional upkeep. Unlock the full value of your data through increasing generation and decreasing operational costs.
AI and ML don’t just facilitate solar asset optimisation; they are asset optimisation.
Kind Regards,
GreenEnco