AI in Utilities | AI in Energy Industry

No more costly surprises. Don’t get caught off guard by asset failures.

It's time to unlock an entirely new approach to maintenance and performance in the power generation industry

Proactively identify anomalous behavior

SparkCognition solutions leverage AI and machine learning at scale that monitor component reliability and provide actionable insights to help you reduce generation losses and prevent catastrophic asset failures before they happen.

Optimize your maintenance planning

Machine learning-based anomaly management works both in real time and over time, helping you plan and bundle work orders for remote sites, reduce unnecessary checks and repairs, and increase asset availability across all operations.

Correct areas of underperformance

Artificial intelligence uses the power of the quantitative data you already have, plus qualitative data from sources like subject matter experts, to help you discover hidden areas of underperformance to be corrected for better production efficiency.

Cutting edge technologies, like AI-based predictive analytics, are key enablers to improving the efficiency of our operations and meeting our ambition to become a net zero company by 2050 or sooner. Working collaboratively with SparkCognition, we have delivered this project in an agile way.

Fereidoun Abbassian,
former VP of Transformation,
Upstream Technology at bp

Reduce unexpected downtime

Monitor component reliability to know when power generating assets are trending toward failure, helping you avoid generation losses. Get notice of pending asset failures up to weeks in advance.

Minimize operational costs

Machine learning models alleviate the cost and burden of traditional model upkeep. They dynamically learn and maintain themselves by adjusting to any component or asset and adapting to changes over time.

Optimize asset performance

SparkCognition solutions identify actionable sources of underperformance in hydropower plants and wind farms, using clustering techniques to separate out instances of underperformance by cause.

Streamline decision making

Create models based on your assets, not generalized manufacturer’s curves, with a data-driven approach to focus on actual observations and system behavior within the contextual environment.

Prevent zero-day cyber attacks

Preventing 99.9% of never-before-seen attacks, SparkCognition provides highly trusted, highly awarded zero-day ransomware protection, virus protection, malware protection, and more.

Rapidly scale AI

Using unsupervised learning techniques, with models that adapt and maintain themselves dynamically whenever components or assets may change, our solutions scale efficiently up and down your operations and across industries.

Use Case Library

Predictive maintenance for fossil power producers

Allowing critical assets to break down is not an option if you plan to remain competitive in the growing energy landscape. AI-powered predictive maintenance offers an advanced, data-driven solution for fossil fuel power producers, helping to reduce unexpected downtime, drive efficiency, and improve profitability for fossil fuel power plants.

Read Use Case

Predictive maintenance for wind power producers

In a volatile and highly competitive market, wind power producers need to be able to monitor component reliability, correct actionable sources of underperformance, and optimize maintenance scheduling in a cost-effective and scalable manner. SparkCognition’s AI-based predictive analytics solution returns actionable insights, improving efficiency, safety, and profitability.

Read Use Case

Predictive maintenance for hydropower producers

Unexpected hydropower asset failures can grind operations to a halt for extensive periods of time. By adopting an AI-powered predictive maintenance paradigm that provides more lead time to identify imminent failures, hydropower producers can mitigate the effects of inevitable asset deterioration and ensure maximum uptime.

Read Use Case