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Utilizing machine learning for risk management enables utilities to reduce costs, promote safety, enhance regulatory compliance, and enhance customer service.
Fremont, CA: Utility businesses are looking for creative ways to better manage risk due to rising regulatory demands, consumer expectations, and increased safety and grid reliability worries. They know that their data has the solutions, but the trick is figuring out how to use it effectively and affordably. Machine learning (ML) can help with that.
Utilities may harness the power of their data to make crucial choices by accelerating the use of artificial intelligence (AI)-driven solutions, notably machine learning. Utility firms can better navigate the current climate and prepare for future use of machine learning to manage risk. Here is how utilities can leverage machine learning for risk management.
• Vegetation management
In order to train machine learning models and give risk ratings to each tree, utilities can compile data from drone video, climatic circumstances, inspection records, and species profiles of trees. Then, these models may be used to rank trees for inspection, suggest the best trimming clearances, and calculate the number of risk occurrences that well-planned pruning would avert.
• Predictive asset management
Huge amounts of equipment-related data, such as brands/suppliers, installation dates, weather data, maintenance logs, and failure history, may be ingested by machine learning methods to forecast when a particular piece of equipment is likely to break.
• Rapid hazard identification
Utilities may use machine learning to evaluate thousands of records from drone footage, lidar, GIS mapping, and a wide range of other sources to spot anomalies that could point to safety risks. Then, human response teams can automatically identify records with issues for more thorough investigation and correction.
• Weather-based outage & demand predictions
Machine learning algorithms may detect weather trends likely to cause outages, demand spikes, and other circumstances by combining historical meteorological data with consumption patterns, outage reports, and other data.
• Maintenance prioritization
Many utilities had historically performed routine maintenance on their equipment or taken action when it malfunctioned. They may prioritize assets by using machine learning to identify those most likely to fail based on their age, the weather in the area, and other characteristics. Utilities may prevent failures that result in outages and fire dangers and perhaps increase the lifespan of their equipment by giving top priority to the assets that are most at risk.
• Guiding grid safety precautions
It could be required to partially shut down an electrical system as a safety measure during extreme weather occurrences. Machine learning provides the swift insights and forecasts utilities need to prioritize the locations most at risk for safety concerns in quickly changing conditions and identify and arrange shutdowns for those areas accordingly.
