The role of AI and the IoT in enhancing solar and wind energy efficiency

The opinions expressed in this article are those of the guest post contributor, and not of Gas Outlook.

The global shift toward renewable energy is driving new approaches to energy production, with solar and wind technologies at the forefront. Yet as adoption grows, so does the need to optimise operational efficiency. The IoT and AI are playing a key role in this effort, enabling smarter, more responsive systems. These technologies offer valuable insights into how renewables can be integrated and managed with greater precision.

Leveraging IoT for smarter infrastructure

IoT devices collect real-time data from connected equipment such as solar panels and wind turbines. These sensors monitor temperature, vibration, solar irradiance, wind speed and system health – delivering detailed performance data across an entire energy system.

In solar farms, IoT helps detect faults or energy losses at the panel or inverter level. In wind applications, sensors track turbine output, blade stress and environmental conditions. This level of visibility allows operators to identify inefficiencies and performance trends far earlier than was previously possible.

Predictive maintenance with AI

AI turns the data collected by IoT devices into actionable insights. Machine learning models trained on operational history can identify subtle anomalies, detect equipment wear and predict component failures before they occur.

In wind energy, AI can forecast gearbox issues using vibration and temperature patterns. In solar arrays, AI models identify soiling, degradation or inverter instability. This predictive maintenance approach minimises downtime and reduces the need for reactive repairs, extending asset lifespans and improving reliability.

Such models align with long-standing practices in the oil and gas sector, where predictive analytics support performance optimisation across drilling and refining processes.

Energy flow and grid interaction

Beyond individual components, AI plays an important part in balancing energy flow across larger systems. Wind and solar outputs are inherently variable, which can complicate integration with traditional power grids. AI-based forecasting models incorporate weather data, satellite inputs and production history to better predict energy availability.

This helps grid operators manage supply and demand fluctuations, optimise storage systems and balance hybrid energy sources more effectively. These insights are particularly relevant as oil and gas companies begin managing or partnering in mixed-resource energy systems.

AI can also support intelligent load forecasting, energy dispatch and pricing strategies – offering a data-driven foundation for entering new energy markets or improving internal energy use.

Cross-sector applications

While the technologies applied in solar and wind systems may seem distinct from traditional hydrocarbons, many underlying principles are familiar. High-resolution monitoring, automation and real-time control are core elements shared by both sectors.

Measurement tools like ultrasonic flow meters – which are widely used in oil and gas applications – are increasingly applied in renewable energy systems as well. In solar thermal installations or wind turbine cooling systems, these meters offer precise, non-invasive flow monitoring. Their role illustrates how existing industrial expertise can be repurposed to support renewable integration.

As companies seek more sustainable operations or explore renewables projects, this overlap between technologies presents opportunities to apply known strategies to new energy models.

Key considerations

The integration of IoT and AI into renewables systems does come with challenges. Data security remains a central concern, especially as the number of connected devices grows. Data quality and standardisation across hardware platforms can also affect the reliability of analytics.

The value of AI depends heavily on the volume and quality of data inputs as well. Incomplete datasets or inconsistent measurements may reduce predictive accuracy. To overcome these limitations, collaboration across technology providers and system operators is essential.

Despite these challenges, the long-term advantages of intelligent systems – greater efficiency, lower maintenance costs and improved planning – are clear. With thoughtful implementation, these tools can be scaled to meet the demands of increasingly complex energy operations.

IoT and AI are transforming how solar and wind energy systems operate, offering predictive insights, performance optimization and responsive grid interaction. For oil and gas professionals, these technologies represent more than just innovations in a parallel sector – they are tools that can support a broader, more integrated energy strategy.

Izzy Rivera is the HVAC & Gas Service Manager at Emerson. Rivera has been involved with ultrasonic flow measurement for 40 years, spanning the history and development of this technology. He was involved in developing the first fully integrated ultrasonic gas metre. He co-founded FLEXIM AMERICAS back in 2005, which is now a part of Emerson Electric.

(Writing by Izzy Rivera; editing by Sophie Davies)