Millennia of sun watching, astronomy, and ever-improving technology allow us to predict quite accurately the day, time, location, and duration of every solar total eclipse. With that prediction, we can plan for the effects of missing rays of sunlight.
US grid operators are now planning for the expected 9,000 MW shortfall in solar output in anticipation of the August 21, 2017 total eclipse. Gas-fired generators are lining up fuel supplies to ensure they can help deliver the missing energy output. Utilities expect huge load swings to occur as solar output drops in near real time with the sun’s occlusion. Preemptive measures such as disconnecting solar generation from the grid and replacing it with other energy sources ahead of the eclipse will allow generators to moderate the effects of the eclipse. After it is disconnected, utilities will carefully plan how to bring solar generation back online. These preventative measures are possible because of the high confidence in predicting the eclipse.
Machine learning can likewise provide high confidence predictions on future asset failures, allowing utilities and renewable energy generators to take appropriate steps to moderate or even avoid the effects of an asset’s failure. Trained models (in significantly shorter time than the millennia it took to develop good eclipse predictions) can deliver predictions at different points in the future for states and conditions both anticipated and unexpected by the machine learning model.
Armed with an asset’s failure prediction, we can deploy machine learning algorithms to optimally (and even automatically) assign O&M teams to take preventative maintenance measures that will address the predicted failure. The algorithms will optimize the maintenance, repair, or replacement tasks for cost, parts availability, skills, time, and even regulatory requirements. With the right models, machine learning algorithms can be as reliable as the sun rising in the east and setting in the west.