Engineers these days can get weighed down by all the possibilities and courses of action on any one project. If on-the-job decisions are made with just ‘gut instinct’ or the crew’s ‘standard procedure,’ outcomes are likely to vary and not keep pace with the needs of utility infrastructures in the years to come. A lack of concrete information in decision-making can cause the failure of projects to meet their goals and respect their budget.  

Implementing predictive analytics may make the difference between success and failure for utility engineering. Guessing and reacting can be replaced with informed predictions and calculated actions. Predictive analytics is a combination of data modeling techniques, machine learning, statistics, and data mining to help ensure the success of utility infrastructure projects. 

What Is Predictive Analytics? 

Predictive analytics moves any type of research from data, to analytics, to informed decision making. It starts with data and then analysis and analytics techniques are applied. This type of data analysis drives future insights. Predictive analytics aims to answer the questions, “What are observations telling us?” and “What actions can we take based on those observations?” 

This new type of applied computer learning is being used in a wide variety of industries to predict the future events and make informed decisions in the present by weighing risks and opportunities. But not only; data modeling techniques, machine learning, and data mining may be used to re-examine past events with unknown causes. Using data and statistics to track occurrences and discover patterns can help all types of professionals to understand events and explain trends. Plus, this knowledge can be used to help solve the problem at hand. 

There are two main types of predictive models. Cluster predictive modelling is focused on identifying relationships between independent input variables. It works to gain insight based on data points that can be clustered or grouped together. In that sense, the output is not always a clear response or dependent variable, but reveals the common traits that the different variables have.  

Another helpful framework is known as the forecast model. This leverages historical data to make predictions and inform our decisions about the future. Classification or regression techniques are used to find dependent variables or predicted output.  

 How Predictive Analytics Can Be Applied to Utility Engineering 

Tracking and maintaining complex assets, such as gas and waterline pipelines and distribution infrastructures, are costly and time-consuming endeavors for power and utility businesses. It demands extensive risk management skills and procedures. The risk presented by a deteriorating length of subsurface pipeline eventually becoming unstable, maybe during a new above-ground construction project, is one example of the unique threats affecting utility engineers.  

All along the networks of gas pipes, are vital components, like clamps, gauges, valves, and machinery that can fail, become damaged or eroded, and will need to be repaired and replaced as they age. Just one faulty point in the utility infrastructure can cause service interruptions, hazardous leaks, and dangerous situations for crew and others.  

Today, predictive analytics can improve the accuracy of locating, prioritizing, and finding solutions even in the power and utility sector. Using applied predictive data techniques is really the next best practice for utility risk management, liability, and safety. Another advantage is that applied analytics helps engineers to avoid the damage and expense of underground utility damage. 

Capabilities enabled by predictive analytics and benchmarking tools may be used to address a variety of challenges in asset management and underground utility maintenance. It can be used, for example, to identify specific points that are at higher risk for causing an outage across a given grid. Then, by integrating other analytic data with other information, such as GISLiDAR3D scanning, and other sources of asset data, computerized modeling can provide a better view of network health.   

This type of overview makes it easier to pinpoint critical areas and the most vulnerable points for maintenance. The data can help engineers predict which pipes will need to be repaired and replaced sooner. Such advancements can enable engineers to be more detailed and precise in project plans and investments. As they continue to adopt this type of technology, it will increasingly influence how they think about utility work. It will help identify where they need to spend the time and attention of their workers, and better focus on those portions of the infrastructure that represent the biggest hazards.  

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