$99.00
This one-hour webinar is Part 3 of a 3-part series. It moves from a discussion of data capture and trend reporting explored in Part 2 to predictive modeling, machine learning, and artificial intelligence as the next levels of battery analytics.
We will examine how machine learning and artificial intelligence can be implemented to identify hidden correlations between disparate data and energy storage system performance, and also independently take pre-emptive action to increase ESS reliability and battery life.
Real-life examples will be shared where predictive models could have flagged anomalous behaviors that were experienced in the field, and led to corrective actions to mitigate unplanned costs and labor.
This webinar will focus on the following key topics:
• Coming to Terms – Understanding the differences between machine learning, artificial intelligence, deep learning, and rule-based systems
• Predictive Modeling Approaches – using data mining and probability to forecast outcomes
• What’s Next – How AI and Machine Learning will impact large-scale battery energy storage
Presenter
Michael Worry – CEO at Nuvation Energy
Michael Worry founded Nuvation in 1997 and has grown the company over 21 years into a thriving electronic products and engineering services firm with offices in Sunnyvale, California and Waterloo, Ontario Canada. He is the CEO of Nuvation Energy, a provider of battery management systems and engineering services for large-scale energy storage systems.
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