Big data and machine learning enable new revenue streams for smart meter operators
CamIn works with early adopters to identify new opportunities enabled by emerging technology.
of CamIn’s project team comprised of leading industry and technology experts
Smart meters have become increasingly ubiquitous. The devices which record information such as consumption of electric energy, voltage levels, current, and power factor are commonplace in households across the world. Indeed, By the end of 2022, there were over 124 million smart meters installed in 78% of U.S. households, according to data released in April.
Table of Contents
Applications areas confirmed
Use cases identified
Week project duration
Our client, a major utility with operations in the electric power sector, was deploying a network of smart meters that could transmit data over a digital network. While these smart meters were installed to monitor customers’ energy usage and bill them accurately, our client recognised that they could also provide valuable data from which they can develop a new product.
As such, they were interested in developing a new product that would fully realise the potential of the data generated by their network of smart meters, but they needed expert guidance to evaluate the wide range of possible applications and business models. They asked us to determine what kinds of applications would be interested in such data insights, who the exact customers would be, and what would be the product or service offering resulted from the smart meter data.
We assembled a project team of four experts, all of whom were selected for their experience with unstructured data that our client’s smart meter network would generate. Our experts helped us perform a comprehensive analysis of smart-meter-based applications and analysis technologies, covering both current solutions and technologies still in development.
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Our team also investigated machine learning algorithms that would allow our client to analyse their smart meter data at scale and create a range of new insights that can be packaged into new products and services. Some of such services included a new tool that would benefit “prosumer” customers, who also produce energy that they provide to the broader electric grid. We also investigated the development of tools to improve demand forecasts and help our client manage their grid more efficiently and effectively.
The team identified additional opportunities, such as creating consumer-facing applications that use their smart meter data, and determining the best network communications technologies to use for their smart meter network. We also identified challenges and risks that our client needed to monitor while developing and deploying their smart meter network, including potential cybersecurity threats.
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Our team’s conclusions illustrated the strengths and weaknesses of each technology relative to our client’s specific goals. Our framing helped them to understand which technologies were most mature, and to consider those that could provide the greatest benefits when integrated with their smart meter equipment and networks. As a result, our client is pursuing the machine learning-based solutions we recommended, and they are now developing internal tools for each use case and each product and service our team identified.
