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Overcoming challenges
A new way to address energy problems with AI .
By Blake Bixler
Artificial intelligence has become a buzzword in many industries , including energy . The term is broad and captures many ways of training machines to perform tasks . Frameworks like neural networks and unsupervised learning are based on correlations - the most common way to develop AI models . These types of AI create probabilistic models that have many challenges that render the AI ineffective in complex problem-solving environments . For example , limited data , inability to identify abnormal behavior appropriately , high-stakes problems , varying delays in cause and effect , and lack of explainability all contribute to correlation-based AI ’ s limitations . In this article , I ’ ll take a closer look at these challenges and propose an alternative - causal AI .
Current correlationbased AI challenges
Let ’ s take a closer look at each of the challenges for applying correlation-based AI in the energy industry :
■ Limited data - Most AI models require thousands of data sets for training . The number of data points and / or availability are often not sufficient .
■ Importance of identifying abnormal behavior - Correlation-based AI models are good at predicting expected behavior in normal situations . However , these models exclude data that does not fit the trend ( outliers ), as this data appears random and skews the model . While we may not have an explanation for the behavior , there are no random events in systems governed by conservation laws .
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