Quite a dichotomy has emerged in the utilities industry over when to develop meter data analytics. As I spoke recently with attendees of the Utilities Analytics Institute’s 2012 Summit, two opposing views emerged.
On one hand, some utility companies have been extremely focused on their AMI and smart meter rollouts – some as a result of their ARRA funding and related deadlines – and haven’t focused on what to do with the data the smart meters generate. Utilities in this camp have been storing the data, but only using it to bill their customers.
The data has not been leveraged for any other purpose and the cost of simply storing it to satisfy regulatory requirements is raising some eyebrows. This has people considering whether there are business benefits in analyzing the data. Summit attendees in this camp seem to have come seeking ideas for how to get business value from their stored data.
In the second group are utility companies that are beginning their smart meter rollouts and, as part of their implementation, are seeking answers to their questions of how best to leverage the smart meter data. Most that I spoke to in this camp came to the Summit with ideas in mind and were seeking to have them validated.
The business case for storing this data has driven them to find business benefits from analyzing the data to offset the cost of simply storing it to keep regulators happy. Hence they are exploring how to create meter data analytics that focus on improving customer management, asset management and overall business performance.
Regardless of which camp they find themselves in, the good news is that they both recognize that there are benefits in developing meter data analytics. And the Summit speakers provided ideas for meter data analytics that fall, roughly, into three categories:
- Customer analytics – these analytics leverage smart meter data so that customer behavior can be analyzed. For the first time in the history of the industry, distributors can literally create actual load curves for every customer. These load curves can be used to segment customers based on their usage patterns and enable the utility to offer attractive price plans to customers while lowering their peak usage. A win-win for both the consumer and the utility!
- Asset analytics – this group of analytics takes the smart meter data and combines it with the distributor’s energy delivery model. Doing this can enable a visualization of how loads are affecting distribution assets over time, allowing utilities to modify how and when they perform preventative maintenance to assets. Where assets are consistently operated near capacity, maintenance can be applied more often thereby reducing unplanned outages. Where assets are consistently under-utilized, maintenance can be delayed and some costs avoided entirely.
- Financial analytics – these analytics seek to protect the revenues of the utility. Smart meter data can be used to systematically detect where non-technical losses (i.e. theft) are likely occurring and enable utilities to take more immediate remedial action. Also, the meter data can be used to improve tracking of the end-to-end meter to cash process, ensuring that the utility is billing every meter every month (or whatever period they choose to bill customers).
While a few utilities are focused on one of these areas, many utilities are building business cases that exploit analytics across each of these categories. However, in either case, Summit attendees agreed that a single, centralized “one version of the truth” was the best approach to managing the data that powers all of the analytics.
So while there is dichotomy about when to focus on meter data analytics, there is much more agreement that business value exists in the smart meter information and, through continual analysis, utility companies can enjoy tremendous amounts of business value across their enterprise, driven by their data.
