Harnessing AI in wind energy: Risks, opportunities, and the role of contractual clarity

Law360 Canada (December 3, 2024, 8:51 AM EST) --

Michael Gallagher

Miguel deMello

Mohammad Ali Raza

The passage of Bill C-49 and amendments to Nova Scotia’s Accord Acts signal a significant opportunity for offshore wind energy investment in the province. As Nova Scotia strives to establish itself as a global leader in offshore wind, understanding how artificial intelligence (AI) can optimize operations and address project-specific risks is critical to success. Major investments are anticipated in the coming years, making it essential for stakeholders to explore AI-driven solutions to maintain competitiveness with leading jurisdictions.

The role of AI in wind energy: global and local contexts

Globally, AI is transforming wind energy by enabling real-time data analysis, predictive maintenance and operational optimization. For example:

• Predictive maintenance: Researchers using deep learning techniques like Long-Short-Term Memory (LSTM) networks and Support Vector Machine (SVM) clustering have accurately predicted failures in turbine components, enabling timely maintenance and reducing costs.
• Performance optimization: Google’s DeepMind improved wind energy’s value by 20 per cent by forecasting power output 36 hours in advance, optimizing grid commitments and increasing predictability.

Locally, Nova Scotia has significant potential to integrate AI into offshore wind (OSW) development. The draft Regional Assessment of Offshore Wind Development in Nova Scotia identifies over 31,200 square kilometres of potential development.

These efforts could be supported by initiatives like DeepSense, an organization that fosters collaboration between AI experts and maritime industries, and Global Spatial Technology Solutions (GSTS), which applies AI and big data to maritime logistics. Nova Scotia could also learn from the U.S. National Offshore Wind Research and Development Consortium, which demonstrates how investment in AI innovation can address engineering challenges and monitoring techniques for OSW projects. A similar commitment to advancing AI applications would help Nova Scotia remain competitive.

Risks associated with AI in wind energy

Despite its advantages, integrating AI into wind energy carries risks, including:

• Algorithmic errors: Incorrect wind predictions can disrupt energy production and grid reliability.
• Cybersecurity threats: AI systems are susceptible to cyberattacks, exposing sensitive data and operations to potential breaches.
• Reduced human oversight: Over-reliance on AI systems can limit the ability to address unforeseen challenges effectively.

These risks highlight the need for robust risk management strategies to ensure AI adoption does not compromise operational reliability or stakeholder trust.

Mitigating risks through legal frameworks

Proactive legal frameworks and contracts play a pivotal role in managing AI-related risks. Clear and enforceable agreements can address key concerns:

• Accountability for AI errors: Contracts should clarify liability for operational failures, such as miscalculations leading to turbine shutdowns or lost revenue.
• Cybersecurity obligations: Agreements must specify measures to protect AI systems from cyberattacks, including data encryption and incident response protocols.
• Data management protocols: Guidelines for data use, sharing and storage ensure compliance with industry and regulatory standards.

For example, if an AI system inaccurately predicts wind conditions, leading to a turbine malfunction, the contract should specify whether the AI provider or the wind farm operator bears responsibility for losses. Similarly, clauses addressing cyberattacks should allocate risks and responsibilities, ensuring all parties are protected. By addressing these scenarios, contracts foster transparency and reduce the likelihood of disputes.

Conclusion

Nova Scotia is poised to become a leader in offshore wind energy, and AI offers transformative opportunities to optimize operations and reduce costs. However, these benefits come with inherent risks that must be managed through strategic investments in technology and comprehensive legal frameworks. By emphasizing accountability, cybersecurity, and data management in contractual arrangements, stakeholders can confidently harness the potential of AI in wind energy while mitigating risks and building trust.

FAQs

1. How does AI improve wind energy production? AI enhances turbine performance, predicts maintenance needs and improves energy forecasting accuracy.
2. What are the main risks of using AI in wind energy? Key risks include algorithmic errors, cybersecurity vulnerabilities and over-reliance on AI without adequate oversight.
3. Why are legal contracts critical in AI-driven wind energy projects? Contracts provide clarity on accountability, data protection and cybersecurity measures, reducing risks and preventing disputes.
4. How can legal professionals assist with AI-related issues in wind energy? Legal experts can draft contracts that address unique challenges posed by AI integration, ensuring compliance and stakeholder protection.
5. What should be included in a contract for AI-powered wind energy projects? Key elements include clauses on AI accountability, data protection, cybersecurity obligations and response protocols for AI-related incidents.

Michael Gallagher (MGallagher@coxandpalmer.com), Miguel deMello (mdemello@coxandpalmer.com) and Mohammad Ali Raza (mraza@coxandpalmer.com) are lawyers in the Halifax office of Cox & Palmer. 

The opinions expressed are those of the author(s) and do not necessarily reflect the views of the author’s firm, its clients, Law360 Canada, LexisNexis Canada or any of its or their respective affiliates. This article is for general information purposes and is not intended to be and should not be taken as legal advice.   

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