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Utility Asset Management and AI

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Artificial Intelligence- or the infamous AI- has toppled the industrial world by opening up a whirlpool of opportunities with data-driven insights to make smarter, success-guaranteed and immaculate decisions. It nurtures core business processes by automating strategic decisions that empower smart homes, cities, and smart infrastructure management. Currently, the industrial world of utilities is using this technology to deploy data-drivenness in a myriad of asset management calls. The emerging use of AI to empower utility infrastructure managers has allowed countries of all sorts to entail approaches that encourage higher asset returns and dodge expensive asset threats and risks by yielding portfolios navigated by data-rich insights. 

This article focuses on how AI has propelled in fabulously facilitating utility infrastructure management in industries like energy, water, wastewater, power, gas, oil, sanitation, stormwater, drainage and other utility service sectors. 

Prior Uncontrollable Challenges Made Controllable and Foreseeable by AI

Utility infrastructure is generally a complex structure of asset components like conduits, poles, vaults, fans, pumps, pipes, valves, compressors, pressure and flow regulators, metres, overhead and underground wires, and more footprints spanning from city-wide to state or countrywide landscapes. Utility infrastructure challenges start at the very beginning, from asset planning, engineering, deploying, utilising, and maintaining to salvaging, upkeep or replacing. Asset spreads of utility industries are very costly and therefore are all the asset practices that come after deploying them. Apart from this grave challenge, utility asset managers encounter many dire responsibilities and limitations. Such as: 

  • Being updated with industry 4.0 innovations and digital transformation that are now mandated in many countries
  • High churns of utility services demands due to the dynamic demands of various utility service consumers
  • Social and political influences like energy transitions to contribute environmental and regulatory policies
  • Stiff competitors entering the market with better inventions and bold market entrances
  • Tackling unprecedented climatic and ecological changes and more.
This is where AI becomes a crucial technology counterpart for asset managers in the utility world. It did not take long for AI-driven asset management to be acknowledged as being a fancy technology solution to mainstream technology. Thus, learning about the importance of AI in the scope of Utility Asset Management is now wisdom that no asset manager can afford to overlook.

AI in Its Essence

Simply put: AI is a simulation of the human brain that trains algorithms to be equipped with an ‘embedded intellect’ to manipulate value-added data to build smart programs and applications for users. The most phenomenal advantage that AI offers is minimising human intervention. Almost any activity of a utility company requires human effort, human intellect and human-made decisions. AI makes these tasks automatic, well-supervised and failure-proof by allowing digital algorithms to train in asset-driven events that implement a data-driven transformative change in making asset decisions.    Utility companies that invest in AI solutions have shown expedited success in optimising bottom-line operations by balancing the risk, cost and performance of assets to fit their asset management requirements. The use of AI is, therefore, a tipping point for most utility technology users, for it landmarks success with data-driven automation and analytics.    Here are some of the key tools powered by AI that have astoundingly influenced the asset management world:
  • Artificial Neural Networks
  • Cluster Analytical Tools
  • Decisions Trees
  • Evolutionary Algorithms / Genetic Algorithms
  • Natural Language Processes
  • Random Forests
  • Support Vector Machines

How Does AI Work in Utility Asset Management

Utility asset managers are frequently limited due to tight budgets, spontaneous asset risks, hard-to-reach asset locations, limited resources, and delayed analytical processes. All these restrictions and burdens are mitigated by AI technology, but how?

You are a dam infrastructure manager…

Imagine you are a water utility asset manager that is in charge of overseeing a catchment area of your city. This means you are supervising the longevity of the dam, current performance, sustenance of the infrastructure in extreme dam events, availability of maintenance crews, 24/7 monitoring of the water levels and a million other responsibilities.

You need to assess dam water levels 24/7

We will be investigating a simple hypothetical example to understand AI applications better. For instance, imagine you are determined to learn when the catchment will meet critical water levels. Here are the four steps of training your AI model.

Evaluate What You Already Know

You can demarcate dam events based on historical information, expert intuition and other information sources. You can learn about the events that can threaten the dam infrastructure, the wildlife and the communities neighbouring the dam structure.  

Collect ‘Right Answers’

Gather and compile pertinent data to aid in analysis. If the goal of learning correct responses to extreme dam water levels consists of “expert” judgement, then so be it. Your team can collaborate to gather correct historical responses to past extreme dam events.

Constructing Algorithms

AI algorithms can be built by technology experts to allow schemas and virtual data structures to train in recognising unique data patterns that hint at critical dam water levels. This will teach programs to self-correct or warn foreseeable threats using data collected from various data points (like databases, applications, IoT sensors of the dam, satellite data, expert insights etc.). 

Make Way for Automation

Once your AI machine is ready, it will continue to be trained with real-time, seamless data inputs. It will be trained to sound alarms, sense anomalies, activate notifications and even control smart dam gates and sluices based on the information fed. The feedback of AI algorithms will be automatically reevaluated and self-reliantly marked by KPIs to adapt to various dam events without any intermediary involvement whatsoever. 

AI and The Utility Service Future

Artificial intelligence systems are used by utility service enablers to enhance the management of sensors, scalable maintenance plans, investment management plans and more. It does not simply limit itself to organisational data but also concerns external influences like ESG (Environmental, Social, and Governance) responsibilities, environmental changes, dynamic asset degradation, service demands, natural disasters and other parameters embedded in utility workflows and processes via this technology. Thus, ensuring a pragmatic solution system driven by historical and real-time data offers futuristic insights for utility infrastructure managers. It is time for your utility organisations to leverage this digital momentum to enhance performance, cut costs, and mitigate asset risks in a single effort. Early AI adopters are changing the utility service world as we speak; are you already a part of this revolution?

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