TSL™ big data platform

Time Series Lab big data platform is designed for Energy Utilities, ESCOs and Energy Data Scientists to proceed terabytes of energy data in more efficient way. The intelligent cloud-computing platform provides the storage and analysis of a raw data to provide consumption insights including statistical and energetic baselining for all types of customers.

Big data analytics

TSL is able to provide the necessary smart grid analysis about :

  • Clients consumption profile
  • Baseline energy consumption
  • Smart grid monitoring – performance and analytics
  • Dissaggregation of clients energy consumption to discover heating or cooling energy usages
  • Short term and long term load forecast for both utility managers and individual clients


The main tasks of BigData platform are to capture, communicate, aggregate, store and analyze big data to provide the statistical reporting for energy utilities and for further exploitation through energy applications developed by GridPocket and their partners.


Collecting, analyzing and managing smart metering data to create valuable information for customers is a complex and challenging task for Energy Utilities. Getting insights from the massive amount of meter data can produce substantial benefits for utilities- increase profit margins, optimize energy supply grid management and accelerate decision making process.

Big data platform

The architecture of cloud-computing big data platform enables the collection of energy data, clustering data, aggregation and more statistical analysis. It is connected to data collection platform built on oneM2M/ETSI M2M premises, which supports the high volume of connected devices and metering systems. 

Scalable data analytics are necessary to assure an effective demand response management and peak load shifting for Energy Utilities. Statistical analysis of historical consumption (resampling, statistical summaries, aggregation, distance measure, variance, etc) developed by data scientists are the key to provide this necessary output.

 The data cleaning for multiple formats, sources and technologies provides rich detail leading to value-added information. Sophisticated analytics can improve decision making and help to create new energy applications.

Big data uses cases in energy :

Energy consumption analysis

Average consumption

energy utilities

  • Customer dashboard web applications
  • Customer Segmentation
  • Profiling consumption using internal and external factors (socio-demographics data, buildings information, weather data, electricity market)
  • Public buildings open energy data
  • Real time smart grid monitoring analytics
  • Consumption prognostics
  • Programmable control applications

  • Demand response management
  • Peak load shifting
  • Smart Grid balance
  • Customers loyalty
  • Consumption transparency
  • Customer engagement
  • Customer profiling
  • Consumption analytics
  • Machine to machine technology
  • Programmable control

More info