Can Artificial Intelligence Cut Down the Risk of Wildfire and Power Outage?
8th Dec, 19
According to the National Interagency Fire Center, there were 46,706 wildfires in the United States of America in the period between January 1 to November 22, 2019, compared with 52,080 wildfires in the same period in 2018. Wildfires and the subsequent power outages caused by them are one of the biggest menaces facing modern America. They can lead to significant loss of property, lawsuits and even to bankruptcy of power companies.
So how can we stop wildfires from happening? The first step is to identify the source of the problem, so let’s take a look at the state of California, USA, the state with the highest number of wildfires in 2019*. According to the California Department of Forestry and Fire Protection, half of the 20 most destructive wildfires in the state’s history have been caused by electrical sparks or powerlines.
Given these facts, it is clear as day that better utility vegetation management can definitely cut down on the risks of wildfires and power outages. The current processes in place for UVM are highly dependent on second-hand data, manual labor and long-drawn-out processes. It is a segment that is crying out for digital transformation, and there is huge potential here to tap into cutting-edge technologies like artificial intelligence (AI).
AI has already made self-driving cars and humanoid robots a reality. It has become a major disruptor for multiple industries and could spark the next industrial revolution by optimizing processes with ‘machine-like’ efficiency. Similarly, the utilities vegetation management space also has a lot to gain from AI platforms such as the AiDash Intelligent Vegetation Management System (IVMS).
IVMS streamlines vegetation management operations tasks by articulating vegetation management operations to the smallest details like ‘what to do’, ‘when to do’, ‘where to do’ and ‘how to do’.
What to do:
Data is gathered primarily from high-resolution multispectral satellite imagery and combined with on-ground field reports. Secondary sources of data collection include LiDAR, airplanes, drones and remote sensing. The AI platform crunches the data related to species growth rate, trim cycles, labor hours, and more to assign a priority level to vegetation management tasks. With more historic data in the system, the predictions become more accurate and the system becomes that much more efficient.
When to do:
The AI-based prioritization engine looks into the backlog of vegetation management tasks in order to map against the available budget and dollar impact of outages. The platform then prepares an initial four-year road map for vegetation management, which will constantly be updated to deal with unpredictable circumstances.
Where to do:
The AI engine also defines where the trimming should be done and which sections in the network should be prioritized. It can also optimize routing so that the manual workforce spends less time and money on traveling from point A to point B.
How to do:
All vegetation management activities can be executed using AR-VR and geo-enabled apps, making review and audit seamless. For example, AR-VR can be used to measure the diameter of a hazard tree that has to be cut while a picture of the tree is taken, thereby making the process of hazard tree work order and task review seamless.
AiDash can provide utilities companies looking to manage distributed assets more visibility (satellite data) and a better brain (AI platform), which when combined lead to greater control over their business outcomes. Traditional vegetation management systems were slow, rigid and inaccurate. AiDash provides a high-tech solution that is dynamic, accurate and easy-to-use. The move to AIVMS can save utilities companies millions of dollars and help them on the path towards digital transformation for the 21st century.