Smart conservation in wind energy: How AI in biodiversity protects birdlife

Artificial intelligence (AI) is emerging as a transformative tool for conservation, helping wind farms minimise ecological impacts through advancements in wildlife monitoring, predictive modelling, and real-time data analysis.

AI’s role in conservation is still evolving, but its potential for positive change in species protection and biodiversity management is already becoming clear.

Species monitoring and conservation

Traditionally, monitoring wildlife around wind farms relied heavily on manual observation, often limited by time and scope. AI is transforming this process with automated, continuous, and highly accurate monitoring systems.

AI can identify species, track their movements, and collect species-specific data, giving conservationists an unparalleled view of wildlife interactions. By analysing images, videos, and acoustic recordings, AI-powered systems offer real-time insights essential for protecting endangered species and proactive conservation planning.

Predictive analytics and collision avoidance

Predictive analytics allow wind farms to anticipate wildlife activity, helping to mitigate risks and align operations with conservation goals. By forecasting high-risk periods for wildlife—such as peak migratory seasons—predictive models empower conservationists and operators to adopt strategies that prevent potential impacts.

For example, Dutch authorities recently implemented a predictive “stop-start measure” for offshore wind farms. This system, tested at the Borssele 1 & 2 and Egmond aan Zee wind farms in May 2023, integrates weather forecasts with bird migration data to predict migration events up to two days in advance. While the model helps reduce collision risks by providing early curtailment warnings, it also illustrates the challenges of predictive modelling, such as false positives that can lead to unnecessary production losses and false negatives that may overlook actual collision risks. Though not fully optimised, such predictive approaches highlight the growing importance of data-driven conservation strategies within renewable energy.

Environmental DNA analysis

As organisms interact with their surroundings, they leave behind organic material containing DNA, known as environmental DNA (eDNA). Analysing eDNA samples enables the detection of various species within an area, providing critical insights for conservation efforts.

AI can further enhance eDNA analysis by improving species detection accuracy and accelerating the identification process. By combining eDNA sampling with AI-driven analysis, wind farms can gain a comprehensive understanding of the local ecosystem, supporting informed biodiversity conservation strategies.

Adaptive management through data-driven decision-making

AI-driven adaptive management supports wind farms in making dynamic, data-informed adjustments to minimise ecological impacts. By continuously analysing environmental data, AI enables real-time operational modifications based on changing conditions, ensuring wind farms are responsive to the needs of local wildlife. This proactive approach helps operators meet regulatory standards and align with biodiversity conservation objectives, supporting a more harmonious relationship between energy production and environmental stewardship.

Towards coexistence: AI’s role in sustainable wind energy

As the demand for wind energy grows, so too does the need for technologies that allow for sustainable coexistence with nature. AI is proving to be a transformative asset in this journey, enhancing traditional monitoring methods, enabling predictive conservation strategies, and supporting management systems that prioritise wildlife protection.

At Spoor, we specialise in developing software solutions that foster coexistence between wind energy and nature. By embracing AI and other advanced tools, the wind energy industry can build a future where renewable energy and biodiversity conservation go hand in hand.