August 2012

Innovations in Comfort, Efficiency, and Safety Solutions.

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Bert TaubeEMAIL INTERVIEWBert Taube and Ken Sinclair

Bert Taube, Director of Business Development, Versant Corporation

Bert Taube leads Versant’s business development efforts to build advanced NoSQL data management and analytics solutions applicable to large and complex data challenges in the energy and smart grid sector. Prior to Versant, Mr. Taube held a variety of senior management positions in energy & automation as well as in semiconductors for companies such as Siemens or Supertex. He developed intelligent project solutions (FACTS, HVDC) integrated in high-voltage power transmission networks. He also served as an advisor, investment & technology consultant for several energy and clean technology startups in Silicon Valley.  Mr. Taube holds a Ph.D. in electrical engineering from the University of Rostock and an MBA from the Kellogg School of Management at Northwestern University.

Big Data, Energy Management and Intelligent Buildings

Veracity is the recognition that the data is not perfect and that achieving “perfect” data has a cost associated with it.

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Sinclair:  What unique characteristics of the utility industry make it an ideal use case for Big Data?

Taube:  The utility industry is unique in that the product is consumed virtually simultaneously to its production (but the price may be set years in advance) and the focus is on the utilization of assets (which are often defined by circumstances) rather than the minimization of assets. In this environment, the acquisition of real-time data can be costly and can seriously impact the bottom line.  The utility industry must not only deal with the volume, velocity, and variety of Big Data but two new “V’s”: validity and veracity.

Validity is adding a fourth “dimension” to Doug Laney’s model, where time is considered. Information in the utility environment often has a “shelf life” and is useful, and therefore needed to be stored, only for a fixed amount of time. After that time, the data may no longer be needed for evaluation. The questions of when to archive or even dispose of data become relevant given the cost of storing large quantities of data.

Veracity is the recognition that the data is not perfect and that achieving “perfect” data has a cost associated with it. The questions become 1) how good must the data be to achieve the necessary level of analysis and 2) at what point does the cost of correcting the data exceed the benefit of obtaining it?

Sinclair:  How has data management technology adapted to fit the needs of the utility industry – and what still needs to be done?

Taube:  The utility industry has come to a point where the data-management technology of the past no longer fits the needs of the industry just at a time when the amounts of data produced are about to increase significantly. What is needed is a data-management technology that is optimized for analysis rather than constraints such as space and speed. Ideally, this database technology would be built much like the grid itself, with classes of assets that have a natural relationship between the classes.

One type of database that has been around for two decades but has not had much penetration in the utility industry is the object-oriented database. This type of database is used in the telecommunications industry and the airline industry to track large number of objects. Unlike relational (SQL) or serialized databases, object-oriented databases offer seamless integration with object-oriented languages. Unlike SQL—which encompasses its own database language apart from the programming language—the object database uses the OO programming language as its data-definition language (DDL) and data-manipulation language (DML). The application objects are the database objects. Query is used for optimization based on use cases, not as the sole means of accessing and manipulating the underlying data. There is no application code needed to manage the connectivity between objects or how they are mapped to the underlying database storage. Object databases use and store object identity directly, bypassing the need for the CPU and memory-expensive set based JOIN operations using SQL. Object databases exhibit traditional database features, such as queries, transaction handling, backup, and recovery, along with advanced features such as distribution and fault tolerance.

Sinclair:  Specifically, how does data management and analytics benefit the electricity and energy sector?

Taube:  NoSQL data management and analytics solutions provide a quantum leap in the effort of the electricity sector to improve the ability of electric power grid operators to detect and react to system faults that would otherwise lead to power disruptions. Versant’s integrated big data management and analytics development solution allows for accommodation methods to quickly identify precursors to impending disruptions of the grid by using a wide variety of data sources and state-of-the-art analytic methods.  Unlike many industries, power delivery is notoriously variable, with daily, weekly and annual variations due to variability in customer load, generation dispatch, delivery system outages and other reasons.  This variability has challenged the industry in discerning patterns that can be used to identify off-normal conditions. NoSQL state-of-the-art data management technology provided by Versant can address these challenges unlike any other data management technique available today and prevalently used in the energy sector so far. It takes advantage of the vast amount of streaming data available to grid operators. The technology can rapidly and reliably screen and analyze data to detect faulty data and correct for missing elements in real time.

Sinclair:  In terms of data management solutions, what lessons has the utilities industry learned from other sectors?

Taube:  The power-delivery segment of the electric utility industry has been historically challenged in applying techniques developed in other industries to their line of business. Techniques like just-in-time inventory methods fail during large weather events, and maximizing utilization of assets fail to take into account the large swing in daily and seasonal loadings. However, there is one area that aligns well with the segment: the military methods applied to the battle field. The scale and diversity of assets, land area involved, and deployment of human assets are all similar in their functions. Recent developments in communications, computational capability, and data availability all lend themselves to a high level of intelligence both on the battlefield and within the utility. The proposed benefits of the “Smart Grid” align well with recent developments in data integration, mining, and fusion.

Unlike process facilities—such as oil refineries, paper pulp mills, and generation stations that have relatively fixed modes of operation that easily lend themselves to more commercialized pattern-recognition methods—power delivery has daily, weekly and annual variations due to customer load variability, generation dispatch, delivery system outages and other reasons.  This variability has challenged the industry in coming up with repeatable patterns. Recent work by EPRI along with Southern Company and other utilities developed the concept of “similar day,” whereby the current system conditions can be compared to previous days with a similar construct. While this concept is still in its infancy, the concept is sound and can be further developed into an automated process. The development of “similar days” can provide a suitable data stream for subsequent analytics such as data mining and fusion. Versant’s integrated big data management and analytics framework provides a perfect platform for the deployment of this approach.

contemporary Sinclair:  How quickly do you think we will see real world smart grid solutions roll out?

Taube:  Today most utilities are in smart grid deployment mode (particularly the IOUs). The Public Utilities Commissions have established smart grid strategic plans for all investor-owned utilities (IOUs) nationwide. Based on those smart grid deployment plans have been put in place. At this point, many utilities have entered the implementation phase. The roll-out of smart grid solutions has been scheduled for the next couple of years as significant investments need to be made. By 2020, we should see a large number of real world smart grid deployments in place.

Sinclair:  How does Big Data fit the needs of the intelligent buildings sector, specifically?

Taube:  The building sector has a need for big data solutions for two reasons:
        1. The granularity of data acquisition and processing has increased substantially because of the necessary steps on the building side to enable open standards based, fully integrated energy control in commercial and industrial as well as residential buildings which exploits every aspect to save energy and operating costs for building owners. This includes the whole subject matter of smart energy profiles for buildings where the level of detail is substantial and object-oriented programming the only practical choice.
        2. The connectivity between buildings will become reality as a major criterion to be fulfilled for deployment of automated demand response. This is needed in order to live up to the energy savings criteria established by the public utilities commissions and others nationwide.
Given the requirements explained above we have a need for handling data complexity, variety, volume in real-time. Buildings (at all levels) represents a major part of the utility load profile. As a consequence, we need to have a detailed measurement system in place based on which real-time analysis and decision making can be implemented. On top of all that, building profiles (and each one by itself is complex) will be benchmarked against each other to make sure that best-in-class solutions will be used in all of them over time and, therefore, the total energy and operating cost will be reduced to the minimum possible.

For more detail see the following slides.

•    Explanation on the big part buildings play in the energy sector

Slide 1

•    6 examples of Echelon using Versant’s technology for energy efficient buildings

o    Echelon chose to go with Versant in 1994 after trying relational database technology during the first few years. They learned relational databases did not have the capability to handle the necessary scalability and complexity of the problem in order to achieve full connectivity and interoperability for smart and energy efficient building automation.

Slide 2

•    Lays out the value that can be incurred if intelligent buildings are properly managed

Slide 3


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