Use Cases

Energy Demand Planning using AI

Learn how to use AI to anticipate demand and energy generation to build accurate forecasts.
In: Use Cases

Today let's learn how to use AI for complex use cases such as anticipating demand and energy generation to build accurate forecasts. Machine Learning helps leverage data to enable energy companies to become safer, more reliable, and, more efficient. The main pain points are:

  • Generating too much energy can lead to energy spillage, while undergeneration can result in widespread blackouts
  • It's difficult to predict the precise behavior of renewable resources such as wind
  • Building optimization solutions with the uncertainty involved can be computationally expensive

IBM developed an Industry Accelerator that combines all the disjointed parts of forecast, optimization, and planning into a seamless and coherent end-to-end solution. I interview Aakanksha Joshi, a Senior Data Scientist from the IBM Data Science team at Client Engineering walk through the use case, with a live demo included.

Demo of Energy Demand Planning

Advantages of using AI for Energy Use Cases

The future of energy and utilities relies on the adoption of AI. Boosting efficiency with sustainable practices – good for the planet and bottom line. See below some proven examples of the usage of AI with its clear business value:

  • Improved forecasting: IBM’s windpower forecast models demonstrated significant improvements in accuracy compared to current models. Increases ranged from 5 to 15 MW per wind farm, which equated to US$300–400K in savings per year.
  • Cut equipment purchases: A fleet-management company saved US$9.5M by achieving 100% availability with fewer vehicles.
  • Better use of resources: An electric utility distribution company improved productivity by 14% through better use of resources.
  • Reduced plant downtime: A power-generation utility reduced planned overhauls by 5% and eliminated 5% of forced outages, saving US$4.6M annually.
  • Improved asset utilization: A large OEM reduced overhaul times from 56 days to 21 days.
  • Better forecasting: IBM’s hydropower forecast models improved accuracy significantly compared to current models. Increases ranged from 47% to 72% per hydro facility, which equated to US$1–6M in savings per year
  • Reduced inventory costs: A power company reduced inventory by 26%, and an electric and water utility achieved 25% in inventory reduction and US$33M in savings.
  • Reduced operating costs: 90% of C-level executives representing 15 countries and 13 industries say weather insights could reduce annual operating costs by 2% or more.

Using Templates to Accelerate AI development

There's a lot that goes into making an AI project successful. You have to know the right use case and value you can expect from it before even starting, understand what data sources are available for training or tuning your model as needed (even if this means understanding how those sources work), pick out which predictive algorithm will best suit expected performance goals--and then there’s actually executing all these instructions when getting started! AI templates can help you get started with AI for your business for the most popular use cases.

Industry Accelerators like the ones offered by IBM are a packaged set of technical assets used to help you tackle your next data science project by addressing your most pressing business challenges. With sample data, notebooks, scripts, a sample application, and more, you can kickstart your own implementation and leverage the power of AI.

Written by
Armand Ruiz
I'm a Director of Data Science at IBM and the founder of NoCode.ai. I love to play tennis, cook, and hike!
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