The 4x4 Possibilities: 4 Smart Ways to Use AI in 4 Areas of Utilities' Operation
image credit: Source: @smbilodeau Credit: Smart Phases Inc.
- Jul 22, 2019 1:54 pm GMT
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4 Smart Ways to Use Artificial Intelligence in 4 Strategic Areas of a Utility to optimize generation, improve resilience and reduce operating costs!
In general, few companies have incorporated AI into their value chains at scale; a majority of companies that had some awareness of AI technologies are still in experimental or pilot phases. In fact, out of the 3,073 respondents in a McKinsey Global Institute (MGI) study, only 20% said they had adopted one or more AI-related technology at scale or in a core part of their business. 10% reported adopting more than two technologies, and only 9% reported adopting machine learning.
In the middle are less digitized industries, including resources and utilities, personal and professional services, and building materials and construction. A combination of factors may account for this. These sectors have been slow to employ digital tools generally, except for some parts of the professional services industry and large construction companies. Some in the sector, for which innovation and productivity growth have lagged, potentially in part due to their domestic focus have not been able to integrate AI yet. Some of these players and particularly a number of small firms would have expected Machine Learning and AI to be too complex, or not applicable at their scale, retarding Artificial Intelligence adoption. But, this AI/ML adoption can turn out to be a strategic move in a changing energy sector.
Artificial intelligence can create value across the value chain in 4 strategic areas for Electric utilities, small or huge.
- Project: Enhance demand and supply prediction, assess the reliability of integrated generation assets, and automate demand-side response, notably with Predictive Analytics
- Produce: Using integrated technologies (e.g. intelligent Energy Storage or others) to optimize preventive maintenance, improve electricity production yield, reduce energy waste, and prevent electricity theft
- Promote: Optimize pricing with time-of-day and dynamic tariffing; match producers and consumers in real-time
- Provide: Automate supplier selection, provide consumption insights, automate customer service with virtual agents, and tailor usage to consumer’s preferences
More specifically, AI can help capture significant gains across the value chain in 4 ways
On top of the 4 areas that can be impacted by AI, there are 4 efficient and smart ways to use Artificial Intelligence, Machine Learning and Hybrid Energy Storage in utilities to optimize generation, improve resilience and reduce operating costs have been identified. They are outlined in the following image:
Out of this "4x4" possibilities, we can think of some examples, amongst many others, of AI-related business impact from current use cases:
- Objective to cut 10% in electricity usage by using deep learning to predict power demand and supply
- Planning for a 15% energy production increase using machine learning and smart sensors to optimize assets’ yield
- 5–15% EBIT improvement to enhance predictive maintenance by using machine learning, automate fault prediction, and increase capital
While AI has the potential to fundamentally reshape the energy sector, significant uncertainty remains about how the technology will develop. For utilities and workers, this might suggest a “wait and see” approach. However, I think there is a need for urgent but clearheaded action to respond to the opportunities and risks that are already apparent.
So, let's use any of the "4x4" possibility to make even more efficient, the way toward the digital utility!
P.S. As with every new wave of technology, we expect to see a pattern of early and late adopters among sectors. Concerning utilities, 6 features of the early pattern of AI adoption have been identified by different studies:
- The first feature is that early AI adopters are from sectors already investing at scale in related technologies, such as cloud services and big data. Those sectors are also at the frontier of digital assets and usage.18 This is a crucial finding, as it suggests that there is limited evidence of sectors and firms catching up when it comes to digitization, as each new generation of tech builds on the previous one.
- Second, independently of sectors, large companies tend to invest in AI faster at scale. This again is typical of digital adoption, in which, for instance, small and mid-sized businesses have typically lagged behind in their decision to invest in new technologies.https://medium.com/@smbilodeau
- Third, early adopters are not specializing in one type of technology. They go broader as they adopt multiple AI tools addressing a number of different use cases at the same time.
- Fourth, companies investing at scale do it close to their core business.
- Fifth, early adopters that adopt at scale tend to be motivated as much by the upside growth potential of AI as they are by cutting costs. AI is not only about process automation but is also used by companies as part of major product and service innovation. This has been the case for early adopters of digital technologies and suggests that AI-driven innovation will be a new source of productivity and may further expand the growing productivity and income gap between high-performing firms and those left behind.19
- Finally, strong executive leadership goes hand in hand with stronger AI adoption.
The author, Stephane Bilodeau, ing., P.Eng, PhD, FEC, is a professional with 20+ years in energy technologies for the power sector, transport, C&I and other sectors. Working and teaming on innovation, notably in energy storage, renewable as well as conventional sources, and artificial intelligence, he is the Founder and Chief Technology Officer, of Smart Phases (Novacab), Fellow of Engineers Canada and expert contributor to Energy Central and to Medium.