Machine Learning in Utilities - Thematic Research

Machine Learning in Utilities - Thematic Research


For six decades’ machine learning (ML) was poised to take off because members of the ‘artificial intelligentsia’ had already come up with the theoretical models that could make it work. The problem was that they were waiting for rich data sets and affordable ‘accelerated computing’ technology to ignite it.

These arrived in 2010.

Now, amid a swirl of hype, machine learning - software that becomes smarter as it trains itself on large amounts of data - is going mainstream, and within five years its deployment will be essential to the survival of companies of all shapes and sizes across all sectors.

Machine learning is an artificial intelligence (AI) technology which allows machines to learn by using algorithms to interpret data from connected ‘things’ to predict outcomes and learn from successes and failures.

There are many other AI technologies - from image recognition to natural language processing, gesture control, context awareness and predictive APIs - but machine learning is where most of the investment community’s funding has flowed in recent years. It is also the technology most likely to allow machines to ultimately surpass the intelligence levels of humans.

Many companies, like Alphabet, have already become ‘AI-first’ companies, with machine learning at their core. At the same time, many ML techniques are getting commoditised by being open sourced and pre-packaged into developer toolkits that anyone can use. This means that the time taken for Alibaba and Baidu to catch-up with Alphabet and Microsoft will be minimal.


  • This report analyses machine learning in utilities.
  • The report highlights some of the global leaders in the top ten AI technologies and identifies the leaders and laggards in the machine learning industry and where do they sit in the value chain.
  • It analyses the main trends across the machine learning sector.
  • It identifies the applications of machine learning in utilities.
  • It provides an industry analysis of the machine learning sector and highlights its timeline.
  • It identifies listed and privately held companies at the forefront of machine learning technology and some of the power utilities actively involved in ML applications.
Reasons to buy
  • The report provides a comprehensive analysis of the present scenario and emerging market trends in the global machine learning industry.
  • To gain insights of the global market leaders and challengers in the machine learning industry and where do they sit in the value chain.
  • Provide detailed information regarding the ten categories of AI software and the machine learning timeline.
  • Extensive analysis of the applications of machine learning in power utilities.
  • Major market players within the machine learning industry are profiled in this report and their action plans are studied thoroughly, which aid in interpreting the competitive outlook of the machine learning sector.

  • Players
  • Technology Briefing
    • Definitions
    • Ten key AI technologies
    • History of machine learning
    • How does deep learning work?
  • Trends
    • Technology trends
    • Macro-economic trends
    • Use case trends
    • Applications of Machine Learning in Utilities
  • Value chain
    • Hardware enablers
      • Optimised networking equipment
      • High end processors
      • Communication chips
      • Embedded chips
    • Software enablers
      • Master data management
      • AI engine
      • Developer tools (APIs and SDKs)
      • Software with embedded AI
  • Industry analysis
    • AI and ML likely to become widespread because too much is open sourced
    • AI and ML are transforming the semiconductors market
    • Timeline
  • Companies section
    • Listed companies
    • Privately held companies
    • Utilities
  • Our "Thematic" research methodology

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