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Machine learning could one day help energy providers better pinpoint failing or compromised components in power grids, or better identify traffic congestion for local authorities, according to a study.

A research project led by MIT describes a technique capable of modelling complex interconnected systems made up of numerous variables that change value over time. By mapping connections in these so-called multiple time series, a Bayesian network can learn to identify anomalies in the data.

Power grids are a perfect case study, Jie Chen, co-author of the paper [PDF] and a research staff member at the MIT-IBM Watson AI Lab, explained on Friday. "A prominent example of the source of multiple time series is the power grid, where each constituent series is the grid state over time, recorded by a sensor deployed at a certain geographic location," he said.