Knowledge Transfer Predictive Models for Power Outage Caused by Various Types of Extreme Weather Events

This study uses transfer learning to propose a model for predicting power outages caused by various extreme weather events that are being exacerbated due to climate change. Transfer learning is a technique that allows a model trained on a significant source domain to be fine-tuned for a target domain with limited data, enabling practical regression or classification even in scarce data scenarios. In Texas, the model was developed using data-rich events like ‘Heat’ and ‘Thunderstorm Wind’ as the source domain to predict relatively data-scarce events such as ‘Excessive Heat’, ‘Winter Storm’, and ‘Flood’ as the target domain. The results indicated significant effectiveness of transfer learning, particularly in transitions such as from ‘Heat’ to ‘Excessive Heat’, ‘Heat’ to ‘Winter Storm’, and ‘Thunderstorm Wind’ to ‘Flood’. This suggests that identifying efficient combinations of source and target weather events is crucial. Moreover, this methodology is anticipated to be applicable in Texas and other states facing similar challenges.