Leveraging the Internet of Things and Analytics for Smart Energy Management

Future Outlook: Revolutionizing Energy
Management with IoT
IoT is expected to play a pivotal role in efficient energy management in the future.
As people walk into a commercial building, devices and assets will gather important information about
their movements. The occupancy of each location at every point of time will also be captured,
transmitted, and stored. The control parameter of the assets and devices will be auto adjusted to levels
that create ergonomically convenient working conditions and simultaneously optimize the consumption
of energy. In offices, unique tagging of desk space to individual employee consumption will further
contribute towards efficient management of energy requirements.
Artificial lighting in the rooms will be oriented in ways that ensure the total illumination in the room is
ergonomically convenient for all the occupants. This will also allow for the best use of the natural light. As
the sunlight in the room increases, artificial illumination is automatically reduced so that the total
illumination remains constant at the desired ergonomic level. This optimized energy consumption of
lighting will be maintained by the energy management system in real time. The results are savings in
lighting costs and enhanced life of the lighting equipment.
Fire sensors can become active in the event of fire and flash the nearest exit. The same fire exits can
detect the number of people in the process of exiting through different routes. The EMS can use this
information to develop and highlight routes to streamline traffic through exit routes, ensuring that more
people reach safety.
With the rising cost of commercial energy, smart energy management systems will be used by
organizations for competitive advantage. As the volume of data collected by smart meters increases over
time, these systems will increasingly leverage the latent information hidden in the data to make smart
choices. Big Data and Hadoop are expected to be extensively used in the process as data scientists begin
to mine this data to develop smarter algorithms and decision making tools. Furthermore, robust
analytical algorithms will be applied across varied scenarios, to identify and prioritize energy optimization
opportunities.
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