Over the past decade, we’ve witnessed remarkable growth in solar energy adoption, with installations popping up on rooftops and across vast solar farms worldwide. However, maximising the efficiency and reliability of solar energy systems has presented its challenges. Intermittency due to weather fluctuations, the complexities of maintaining large-scale installations, and the need for seamless integration with existing power grids are just a few hurdles we’ve faced. But what if we could predict equipment failures before they happen? What if we could optimise energy production in real-time based on weather patterns and grid demands?
The answer lies in the powerful combination of Artificial Intelligence (AI) and the Internet of Things (IoT). These technologies are transforming solar energy management, promising to unlock unprecedented levels of efficiency, reduce costs dramatically, and ultimately pave the way for a more stable and sustainable energy future.
IoT in solar
The Internet of Things (IoT) is the backbone of this transformation, providing the crucial data that fuels AI’s intelligence. In solar energy, IoT refers to a network of interconnected devices deployed across solar installations, all working in concert to collect and transmit real-time data. It gives solar panels a nervous system, allowing them to sense their environment and communicate vital information back to a central hub.
IoT in solar involves strategically placing smart devices throughout the solar energy ecosystem. These devices range from simple temperature sensors attached to individual panels to sophisticated weather stations monitoring local conditions. Smart inverters, the workhorses that convert DC electricity from the panels to usable AC power, are also becoming increasingly intelligent. They are equipped with IoT connectivity to report their performance and status. Trackers that adjust the angle of solar panels to follow the sun’s path are now integrated with IoT, enabling remote control and optimisation.
Data collection and monitoring
The sheer volume and variety of data these IoT devices collect are staggering. We’re talking about everything from the amount of energy each panel produces and its operating temperature to the ambient temperature, solar irradiance (the amount of solar radiation hitting the panels), wind speed, and even the status of the electrical grid itself. This data is transmitted wirelessly via cellular networks or satellite connections to a central data platform, typically a cloud-based system. This centralised platform acts as the brain center, aggregating and storing all the information collected from the distributed network of IoT devices.
Remote monitoring and control
The ability to remotely monitor and control solar energy systems is one of the most significant advantages of IoT. Operators can access real-time performance data from anywhere with an internet connection, tracking energy production, identifying potential faults, and predicting maintenance needs. For example, if a panel is underperforming, the system can flag it immediately, alerting technicians to a potential issue. Beyond monitoring, IoT also enables remote control. Operators can adjust panel angles to optimise energy capture based on the sun’s position, fine-tune inverter settings for maximum efficiency, and even remotely shut down systems in case of emergencies. This level of granular control and real-time visibility was simply not possible with traditional solar energy management systems.
AI in solar energy
AI refers to using sophisticated algorithms, primarily machine learning and deep learning, to analyse the vast amounts of data collected by IoT devices. These can identify patterns, make predictions, and optimise system performance in real time. For example, machine learning models can be trained on historical data to recognise trends and anomalies. In contrast, deep learning models can tackle more intricate tasks like image recognition for panel defect detection.
As seen at Amazon’s Baldy Mesa solar farm, powered by Amazon Web Services (AWS), ML models are being used to predict when and how battery energy storage systems (BESS) should charge and discharge energy back to the grid.
Predictive maintenance
Another impactful application of AI in solar energy is predictive maintenance. As AI’s importance and energy demand grow, integrating it into renewable energy generation becomes vital for efficiency gains. AI is already changing the predictive maintenance of solar panels. Cutting-edge machine learning models continuously monitor and analyse data from solar installations, reducing downtime and maintenance costs while extending the lifespan of solar equipment.
To swiftly identify issues, AI monitors a lot of factors, including:
- Temperatures
- Irradiance
- Orientation
- Tilt angle
- Humidity
- Rainfall
- Dirt accumulation
- Power output
- Inverter efficiency
- Operational loads
The Centre for Energy and Materials highlights that predictive maintenance can significantly improve operational efficiency, with potential gains of increasing productivity by 25%, reducing breakdowns by 70% and lowering maintenance costs by 25%. This is achieved by continuously analysing data streams that reveal subtle signs of potential equipment failures long before they manifest.
Furthermore, data-driven analytics tools, such as Univers Solar Advanced Analytics, exemplify the practical applications of AI in this area. By analysing corrective actions across a vast fleet of solar installations, these tools can identify seasonal patterns and recurring issues, providing data-driven recommendations for corrective actions. For instance, Univers’ analysis of over 300 sites, 28,000 devices, and 11GW of capacity showed distinct seasonal patterns in corrective actions related to inverters, trackers, DC health, sensors, grid, and data availability. This level of granular insight allows for proactive maintenance scheduling and targeted interventions, minimising downtime and maximising the operational lifespan of solar assets.
Energy forecasting and optimisation
The changing weather conditions are a significant challenge for grid integration. AI-powered energy forecasting can address this by analysing weather patterns, historical data, and real-time sensor readings to predict solar energy production with better accuracy. This lets grid operators anticipate fluctuations in supply and demand, allowing them to balance the grid more effectively. Furthermore, AI algorithms can optimise energy storage systems by predicting future energy production and consumption patterns.
Companies like Solcast, a leader in solar forecasting and modeling (now part of DNV), use sophisticated AI algorithms to analyse meteorological and atmospheric variables, generating precise solar irradiance and power forecasts. These advanced tools, available for commercial applications and even free home PV forecasts, provide crucial insights for optimising energy production and grid management.
Grid integration and smart grids
Integrating large amounts of solar energy into existing power grids requires sophisticated control and coordination. AI and IoT play a vital role in allowing this integration by facilitating the development of smart grids. Smart grids use AI algorithms to analyse real-time data from various sources, including solar installations, substations, and consumer devices, to balance energy supply and demand. AI can optimise the flow of electricity, prevent grid congestion and improve grid stability. It can also enable demand response programs, where consumers are incentivised to adjust their energy consumption during peak demand periods, further enhancing grid stability and reducing reliance on fossil fuel-based power plants.
Tech taking charge…
AI and IoT are no longer futuristic concepts in solar energy, but they are now the driving force behind its present and future. From smarter maintenance to optimised grids, these technologies are making solar more efficient and reliable than ever. Embracing these advancements doesn’t mean harnessing the sun’s power alone, but we’re also unlocking its full potential for a cleaner future.
