on the topic of energy data

this past week BusinessWire posted a press release from Silver Springs Networks on the availability of two new software applications for energy demand management. without directly endorsing Silver Spring, i’m certainly all in favor of placing information directly in the hands of consumers and these applications facilitate this. regarding the CustomerIQ web portal, this is the telling point of the pr:

CustomerIQ Web Portal 1.5 enables utilities to directly engage customers to reduce support costs and deliver energy efficiency improvements in meeting overall demand reduction goals. The completely redesigned user interface of CustomerIQ 1.5 transforms the customer’s experience by delivering tangible and substantial value from their smart meters through deep insights into their energy usage and costs. Additionally, normative comparisons to like homes in their neighborhood and compatibility with mobile devices like smart phones and tablets engage customers and provide improved ease of use. [Emphasis mine.]

the only way to truly transform data into usable information – delivering tangible and substantial value – is through direct involvement of the utility – utilities to directly engage customers. they are the ones who posses the data and beyond annual reports (which provide no detail below aggregations) to the DOE, per regulation, the data remains in the possession of the utility. as a result, they are the only ones who could possibly facilitate any accurate comparison – normative comparisons to like homes in their neighborhood.

i would be interested to know the limitations of the data. for instance, as of 2007 [most recent date reliable figures are available], there were 3,273 traditional electric utilities in the US. this includes investor-owned, publicly-owned, cooperatives, and federal utilities. the sheer number makes it impossible to obtain data without cherry-picking specific markets, let alone transform that data into information as the formats, granularity, dimensions are variable and often non-standard. adding to the complexity is the myriad of policies, laws, mandates, regulations, integration methods, business rules, and VEE procedures among others. given that utilities span all states, districts, control areas, etc., it’s a daunting task simply to define an accuracy threshold and baseline that establishes a value proposition. this statement from the Energy Information Agency (EIA) reinforces what I’m saying:

State public service commissions have jurisdiction primarily over the large, vertically integrated, investor-owned electric utilities that own more than 38 percent of the Nation’s generating capacity and serve about 71 percent of ultimate consumers. There are 210 investor-owned electric utilities, 2,009 publicly-owned electric utilities, 883 consumer-owned rural electric cooperatives, and 9 Federal electric utilities. A small amount of electricity is sold by generating facilities directly to end use customers. At least 6 States regulate cooperatives, and at least 7 States regulate municipal electric utilities; many State legislatures, however, defer this control to local municipal officials or cooperative members.

of course obtaining data from any utility requires that utility to be on-the-ball with their data strategies – and they are faced with significant challenges that impact quality on the end-users; the following snapshot is from an online article posted to PowerGrid International by a member of IEEE’s 802.15 Working Group on Wireless Personal Area Networks, P2030 Work Group Task Force 3 and the ZigBee Alliance:

The residue of the march toward a demand-responsive smart grid—a multisource fabric of power generation that is circularly interlinked with AMI gateways to home area networks (HANs) that produce load profiles and power factors across a range of appliances—is data, lots and lots and lots of data, perhaps in the hundreds [or thousands] of megabytes per meter per year.
AMI frequently records data on more types of events than utilities have attempted to gather from their business and residential users. This means that a utility must anticipate the kind of storage and processing footprints that its MDM approach imposes on its information technology (IT) infrastructure.
The volume of data from head-end servers must be securely stored, and tasks such as data validation, synchronization, estimation and editing must be carried out to ensure that automated back-office and delivery systems are accurately informed in their actions.
“The data synchronization issue alone is significant,” said Mark A. Ortiz, enterprise architect focused on smart grid application architecture with Consumers Energy, an electric and natural gas utility based in Jackson, Mich. “If a utility doesn’t have a good strategy in place, the potential to duplicate data is real. The volume of meter data including events is tremendous and will quickly increase as you consider scenarios for millions of meters. It is important to understand where customer and meter asset data belongs and avoid duplicating it, preventing scalability issues and costly storage footprints.”
Given that AMI and the smart grid naturally render IT a much larger portion of a utility’s operations, some are taking a divide-and-conquer approach to the task of MDM. Packaged, lightweight solutions for integrating multivendor meter reading and control have emerged. They are designed to free utility IT personnel to concentrate on interpreting and archiving data. Other utilities will find it more valuable to build their own MDM systems tailored to their own unique environments.

it is important to remember that data is data – it’s raw, un-validated and subject to obfuscation through acquisition and internal business & technical processes. once data comes out of the oven, after following a well planned and tested recipe, it is transformed into information – and information leads to knowledge and knowledge is empowering.


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