Project Description


Cloud Energy is the IoT solution for energy monitoring. It integrates energy data with the daily business operations, simplifies the energy budget metering, and supports data-driven choices. It is a modular system for energy process analysis that can be integrated with sensors, software and business processes.


The IoT adds value to energy monitoring systems, by joining the real-time consumption monitoring with the control of production, logistic, and business processes. Not only energy metering, but a comprehensive system to get detailed information and efficiently manage a crucial cost center.


Cloud Energy is based on the Stoorm5 IoT platform, thus it is possible to integrate and extend it both with other Stoorm5 products and with third-party services. The current evolution of control system is towards the interoperability of multiple systems, managing and storing heterogeneous data. Only an IoT-based system can efficiently manage the huge amount of data these systems generate.

An energy control system to support business processes

Interoperable with business services and extendable with other IoT modules

Energy metering

Both in real-time and based on historical data. Time-machine feature for a posteriori analysis

Anomaly detection

Check anomalies based on value analysis and gaps in comparison with model-based data

Economic benefits

Check consumption gaps to verify investments and select among different alternative solutions.

Budget management

Planning of investments and cost center optimization based on consumption trends with proactive adjustments

Differentiated monitoring of each element of machines, engines, milling machines, pumps, and conveyor belts. Energy data in relation to machine shift, worker shift, or product type. Predictive control, planned maintenance, detection of anomalies, wearing of electromechanical parts. Comparison of investments of different machines, production lines, sites.
Selective monitoring of illumination, refrigerators, and ovens. Energy data in relation to retail store, outdoor temperature, number of customers, frequency of refrigerator aisle openingsā€¦ Comparison among devices in the same or different retail stores, benefits of investments. Predictive control, detection of anomalies, equipment efficiency control.
Large Retailers