Six ways AI is accelerating clean energy opportunity

Examples: machine learning used in sensor-connected powers plants; AI algorithms for weather pattern forecasting and energy distribution adjustment; AI used to automate research into development and usage of new materials for solar panels; AI usage for grid infrastructure maintenance.

Artificial intelligence has a key role to play in the energy transition with studies reporting that 93 per cent of environmental UN SDGs will be achievable with the support of AI-powered solutions. We highlight six ways in which clean energy opportunity is being accelerated thanks to AI.

Intelligent wind farms

New wind farm locations benefit from AI algorithms that analyse topography, wind patterns and energy demand in order to select the optimal spot to site turbines so they capture the maximum volume of wind energy. Machine learning-based solutions can even detect the slightest blade misalignment and report back the extent and direction for immediate correction. Wind energy efficiency from offshore farms is also being boosted by remote AI and machine learning technology that co-ordinates the turbine movements of an entire array to function in unison, rather than as solo units.

This helps minimise the flow of turbulent air through the array and maximises power generation capabilities. AI is also useful for production forecasting, which allows operators to increase underperforming asset revenues. In addition, intelligent wind farms can mitigate losses when turbines are offline, by redistributing resources to maintain overall generating capacity. Operational technology cybersecurity capabilities are another area where AI adds value and supports the protection of online and offline assets from external threats.

Weather forecasting

Growing demand for renewable energy solutions will drive future dependence with the challenge of intermittent solar and wind generation impacting potential supply. This is where AI can prove to be an invaluable weather pattern mapping tool to evaluate when and how much power can be generated, and when back-up power will be required. Machine learning can enhance the accuracy of weather forecasting models by identifying historical data patterns to predict temperature changes, rainfall and storms. Site-specific prediction models like the one developed by Google subsidiary, DeepMind, analyse vast amounts of data to be able to ‘nowcast’ imminent weather patterns unfolding in the next few hours. Where AI is less effective is mid-range forecasting, but Chinese researchers are working on a 3D neural network model that has the capability to create accurate mid-range global weather forecasts.

Energy grid resilience

Smart grids that utilise distributed energy resource (DER) assets benefit from AI algorithms that optimise co-ordination between different grid components such as power generation, storage systems and consumer demand. By leveraging real-time data and predictive analytics, supply and demand is balanced and peak load managed, which results in more efficient grid operations, reduced transmission losses and improved grid reliability and resilience. AI software installed in decentralised energy systems using renewable power can also send excess electricity to the grid, with utilities then able to send power to where it’s needed most. In tandem, AI can introduce new efficiencies to lower costs, reduce waste, and cut carbon emissions.

Solar site selection

Finding and analysing potential solar farm sites is subject to numerous environmental considerations where the wrong location can impact production and storage capabilities. The role of AI in analysing colossal amounts of geographical and environmental data is therefore critical in identifying sites with optimal resources and best-fit conditions. Before breaking ground, iterative and 4D design-created digital twin site models and construction plans have the capability to deliver minutely detailed site-specific information critical for equipment selection, scenario testing and layout verification. From a cost perspective, AI can support on-time delivery and help avoid issues linked to last-minute project scope changes and redeploy resources where needed. AI can also confirm the viability of connections to existing grid infrastructure down to array positioning.

Smart power plants

AI-enabled systems can transform hydroelectric, thermal, marine and hybrid power plants with the capability to monitor operations and performance data gathered from site-based networks of sensors. Data analysis delivers a host of transformative benefits from predictive maintenance alerts for downtime mitigation to automatic increases or decreases in power generation to balance peak and low energy demand scenarios. AI is also able to take informed data-driven decisions that can reduce emissions-influencing airflow in waste-to-energy plants and predict – and address - CO2 peaks in hybrid or transitioning plants.

In small-scale plants where manpower is split between multiple tasks, AI can run on autopilot when operators are required to conduct site inspections or maintenance checks. In the future, it is expected that AI will be able to operate full-scale commercial autonomous power plants.

End user education

By analysing consumer behaviour patterns AI is primed to support efficient management of energy demand and predict peak demand periods to optimise energy distribution. As well as overall energy consumption and carbon education benefits, AI data is being used to keep end users informed and aware. For example, automated reports help customers to understand their consumption levels and, more importantly, make changes in their daily habits to reduce usage during peak demand periods. Similarly, utilities companies and governments can use AI-generated data to inform public and customer awareness campaigns. IoT smart home devices such as Amazon Alexa and Google Nest offer AI-integrated solutions that can automatically turn off appliances when not in use or during expensive peak usage hours; set up energy-efficient schedules; and provide remote thermostat control access.