Case Studies > Energy Smart Generation by Unatec using Carriots

Energy Smart Generation by Unatec using Carriots

 Energy Smart Generation by Unatec using Carriots - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Infrastructure as a Service (IaaS)
Applicable Industries
  • Utilities
Applicable Functions
  • Process Manufacturing
Use Cases
  • Machine Condition Monitoring
The Customer
CETASA EOLICA and Gas Natural Unión Fenosa
About The Customer
CETASA EOLICA and Gas Natural Unión Fenosa
The Challenge

Energy generation and management, when not optimized, severely damages the results. If costs are not closely controlled or the production is not predicted accurately, profit can be dramatically diminished or even lead to losses. It is therefore mandatory for energy producers to optimize the management to maximize profitability.

The Solution

The system uses different prediction services and compare them using Carriots to increase forecasting accuracy. The prediction model receives feedback to continuously enhance accuracy in the energy offering calculation. Production breaks (scheduled or not) are included in the predictions. A customized set of KPI's is permanently tracked and displayed in real-time graphs to compare production with predictions and generate customized performance reports, budget tracking, plants comparison and cost reductions.

Data Collected
Alarms For Automated Applications, Downtime, Energy Consumption Rate, Process Parameters, Production Efficiency
Operational Impact
  • [Process Optimization - Real Time Monitoring]
    Deviations and energy quality parameters are controlled in real-time avoiding penalties with sensors linked to the smart metering system, firing alarms via SMS or email.
  • [Cost Reduction - Energy]
    Maintenance is scheduled at optimal times using prediction and production data along with the energy price.
  • [Management Effectiveness - Big Data Analysis]
    A local SCADA connection monitors the internal parameters and analyzes the generated big data to prevent outages and technical issues.

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