Utilities industry use cases leveraging AI, IoT, and cloud technologies

Utilities industry use cases leveraging AI, IoT, and cloud technologies

Utilities need to rapidly transform how they operate to cater to and adapt to evolving needs around electric mobility, the rising demand for sustainable energy sources, electric grid modernization, and customer satisfaction. The utilities industry is facing a time of challenge and change with new constraints ranging from extreme weather conditions to changing regulations and customer (both consumer and B2B) expectations.

Common challenges faced by utilities industry

According to industry research forecast, the global utility market will reach nearly $6 trillion in 2025 at a CAGR of 7%. To achieve this, utilities need to embrace digital transformation and overcome some key challenges such as the ability to:

  1. Increase operational profitability, efficiency, and resiliency
  2. Reduce carbon emissions
  3. Optimize renewable energy production
  4. Transform their workforce with enhanced productivity
  5. Expand market position with innovations
  6. Create new business models

Improve utility business efficiency with modern solutions

By leveraging cloud, data, and AI expertise from the right partners, utilities can create roadmaps and strategies to help optimize their business and extract more value from their existing or yet-to-be-captured grid, IoT and customer data.

There are several solutions that allow utilities to activate their data to improve forecast accuracy, scale with automation, leverage IoT and machine learning, and more.

The type of use cases those cloud, AI, and IoT technologies can help support can be broadly categorized, throughout the utility value chain across the following three main themes:

  • System and business analysis & planning
  • Maintenance and field operations
  • Customer experience

The illustration below depicts the typical use cases in each of those themes.

Improving utility business efficiency illustration

Let’s quickly review those use cases:

  • System-level load impact analysis 

Estimate system-level impact of individual loads to better forecast demand.  

  • Smart grid management 

Leverage AI to help automate existing systems while balancing costs and carbon emissions.  

  • Load curve analysis 

Deploy ML techniques to understand the variation in demand over time for a given energy source.  

  • Carbon reduction planning 

Reduce carbon emissions at the electrical grid level using data science techniques and simulations.  

  • ESG planning and reporting 

Leverage advanced analytics to deliver a holistic solution to analyze ESG drivers, predict footprint, and improve traceability.  

  • Long horizon customer demand forecasting 

Leverage demand forecasting to accurately estimate the rise in demand driven by both existing and new customers.  

  • Preventative maintenance and maintenance forecasting 

Maintain operational continuity and reduce emergency failure events using ML algorithms.  

  • Procurement analytics and optimization 

Leverage AI and ML models to identify redundancies in supply chain and reduce operational cost.  

  • Field asset history 360-degree 

Understand full performance and maintenance history of an asset with a 360-degree dashboard.  

  • Improved asset failure risk management 

Train an ML model to optimize service reliability through better asset risk management and maintenance. 

  • Service call forecasting 

Deploy forecasting algorithms to improve service call forecasts and optimize staff scheduling.   

  • Enhanced billing quality assurance 

Reduce time and labor costs associated with incorrect billing to boost customer satisfaction.  

  • 360-degree customer overview 

Create a holistic view of a customer’s data and enable personalization using dashboards.  

  • Optimize advocacy and rebate targeting 

Leverage data science techniques to improve customer outcomes and the effectiveness of customer advocacy programs.  

  • Improved targeting of payment plan programs 

Improve customer satisfaction by designing appropriate payment plan programs using data science techniques.  


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