Three practical applications of deep learning and IoT in oil and gas


A real transformation is happening in the grid. With the digitization and innovative energy technology catching up it is now possible to economically generate power from Microgrid. Microgrids are beginning to emerge in residential communities, office park, university, military base etc. This has been accelerated by a number of local and global factors converging together – government regulations/incentives, energy consumers are becoming more concerned about their local power quality and efficiency of a system. Industries and businesses have been economically impacted by local brownouts which are a nuisance In this context IOT and digital intelligence plays a major role in solving some of these problems. In this whitepaper, we cover 3 reasons why IOT digital intelligence matters for Microgrid.


Reason-1: Prosumer Intelligence

The foundational building block of a Microgrid is the prosumer. A prosumer consumers energy and contributes to the energy grid. If the prosumers behaviour is modelled at a fine level of granularity a lot of Microgrid risks are mitigated. For example in Cerebra while algorithmically scoring prosumers behaviour it takes into account 4 distinct factors

  • Quantity of contribution
  • Quality of contribution
  • Predictable regularity of the contribution habits
  • Quantity of consumption

All of this is modeled using instantaneous power, current and voltage parameters typically at 15 min intervals or lower collected by the MDM Systems. This algorithmic prosumer can drive surgical actions to stabilize Microgrid outcomes and impact the overall economics

Reason-2: Microgrid Assets

A Microgrid consists of a variety of assets which are configured and orchestrated to deliver localized power. This could Local low-voltage (LV) and even medium-voltage (MV) distribution systems Distributed energy resources (DERs, e.g. microturbines, fuel cells, photovoltaic), Storage devices (flywheels, energy capacitors, and batteries) in order to satisfy the demands of energy consumers. Each of these assets can be diagnosed from a health perspective by deeply examining the digital IOT sensor data which are collected in SCADA/Historian systems and traditionally the signals are unexamined. Cerebra’s signal detection algorithms can forensically surface asset signals which reduce downtime and enhance asset life.

Reason-3: Economics

At Flutura we have a simple formula

Prosumer behaviour intelligence + DER Asset intelligence = Microgrid economics

By carefully examining grid contribution signals in the instantaneous parameters coming from MDM systems and the asset stability signals from SCADA/PLCE sensor streams, the operator can dramatically impact the economic viability of executing a Microgrid.

Closing thoughts

Microgrids are one of the models for an alternate energy future. The economic viability of Microgrid operator is directly correlated to the Digital intelligence maturity. This calls for a comprehensive IOT digital implementation strategy for microgrids which ingests sensor, meter, and ambient weather data to dramatically impact and guarantee asset stability/contribution behavior. Flutura with its next generation IOT intelligence platform tuned to Microgrid use cases combined with its strategic relationship with Electric Power Research Institute (EPRI – Palo Alto & Orlando) is ideally positioned to intercept the future.



Join our mailing list to stay up to date on the latest developments in the IIoT space.