Column
Weinhold’s Power Lines
Data Analytics:
The Power of Prediction
The energy system I studied as a young engineer consisted of just three major elements: central large-scale conventional power plants, grids, and loads. Only pumped
hydro was used for energy storage in public grids (with
the exception of very few battery-based or compressed air
energy storage installations). Loads could be predicted
well, and any remaining uncertainties or sudden incidents
were balanced out using the flexibility of these power
plants and industrial loads across transmission networks.
In many parts of the world, we are now witnessing transformational changes to the energy system, including the
widespread integration of renewable power.
Energy market players must anticipate the intake of wind
or solar and the development of wholesale market prices
by processing large amounts of sensor information, including highly specific meteorological data – a new business
field. Recently, I had the opportunity to visit a startup
company that provides such information based on raw data from four meteorological services to utilities, grid
­operators, and other interested parties. The more refined
the models are, and the more experience and data these
analytics companies can gather, the better they become
at forecasting.
Our increasingly complex energy system is no longer conceivable without data analytics. Here’s an example of how
these developments could soon play out in everyday life.
Imagine a neighborhood in which electric cars have become
fashionable, where many people with the same pattern
of living all come home and charge their cars at the same
time. You’d be looking not at 3 kilowatts of charging, but
at 40 or 50 kilowatts. It’s easy to see how even small populations could quickly have a great impact, and why the
ability to coordinate and make predictions becomes crucial.
The same might be said for other infrastructures such as
heating systems, microgrids in private homes, or complex
industrial setups, all of which require process analytics to
operate efficiently. Data analysis not only deals with complexity, but also facilitates many everyday conveniences.
With that, I’ve already described a key part of the value
streams being created. Our energy systems involve more
and more sensors, communications lines, and data storage capacities; we now also have the massive computing
power required, in combination with cloud-based data
warehousing. Those trends are bound to increase: We will
30 Living Energy · No. 12 | July 2015
see more “smart sensors” with better connectivity, computing power, and bandwidth to digest the raw data via
fine-tuned algorithms.
Analytics companies take the data – the “haystack,” as it
were – and their clients’ questions to help them find the
“needle,” or even cross-correlations between various
“haystacks” of data sets. Thus, big data and analytics enable us to make better-informed decisions despite increased complexity and uncertainty and deliver smart
services with ever-greater precision.
This data is collected unstructured from various sources.
For instance, power plant service staff are required to file
reports that contain valuable information for establishing
best practices. What’s more, the value chain from sensors,
connectivity, data storage, and analytics to derivation of
information is found in many other sectors where patterns
and images must be identified.
A clear picture can be gained even from vague data points.
This raises privacy concerns, since such information can
be used by actors with all kinds of business models or intentions to build profiles of customers, for example. We
know that meta-information on telephone connections
can be analyzed to reveal almost everything about a person’s life. In the energy sector, data about electricity usage
patterns, when correlated with other data, will soon be
as valuable as the electricity itself. Often, the default option is to collect the data and then see how it can be used.
Clearly, regulators and policymakers have their work cut
out for them, and for ­Siemens, cybersecurity is also a
­major focus.
Nevertheless, the advantages are just as obvious: With the
correct information, product design and development can
be improved; service and spare parts can be delivered in
a more targeted manner; the resilience of infrastructures
can be enhanced; and climate change may be mitigated
by improving coordination between conventional power
plants, renewable power plants, energy storage, and loads –
for example, with Virtual Power Plant technologies.
There is a global swarm to push back the boundaries of
data analytics. Sensors, communications, and processing
power are all improving, and information is processed
to ever-finer granularity. What will we end up doing with
it? Despite all our computing power, that’s a difficult
one to predict. p
Illustration: Elisabeth Moch, Photo: Jan Awerverser
Michael Weinhold
CTO ­Siemens Energy Management
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