– Bert Taube and Ken
Bert Taube, Director of Business Development, Versant
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.
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.
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
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?
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
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?
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?
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
Sinclair: How quickly do you think we will see
real world smart grid solutions roll out?
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?
The building sector has a need for big data solutions for two reasons:
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.
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
6 examples of Echelon using Versant’s technology for energy efficient
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
• Lays out the value
that can be incurred if intelligent buildings are properly managed
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