Machine learning: analytics

Machine learning algorithms can help utilities address with a broad range of practical and strategic switch providers
meters problems, grid operations to accommodate fluctuating levels of renewable resources


MACHINE LEARNING EXAMPLE
 For instance,  some instances of energy theft identified, then you can feed into your analytics system data from those cases and have it look for similar patterns in current customer data. The system will return possible hits and offer a confidence rating for each. That confidence rating can help you decide whether you need to roll a truck to check out a certain instance of possible theft. Then, when you investigate the situation and feed the results back into your system, you fine-tune the algorithm."

Machine learning algorithms can also reveal new useful patterns in AMI data. That is, energy data can start to speak for itself, in ways that help utilities plan better.

For instance,  unnoticed patterns of momentary outages or other grid issues might help a utility better predict maintenance needs for transmission and distribution assets. "If you want to figure out which transformers will fail, you can feed in data about which ones have failed already, and the parts of the network they function within -- and then let the system reveal correlations,"

Utilities can start to capitalize on machine learning-enhanced analytics even if they haven't yet deployed smart meters or meter data management technology.