Advanced machine learning for factory optimization

Energy consumption is a key asset of industrial facilities especially with regard to its optimization. The energy cost is continuously increasing also due to external worldwide factors.Moreover, consuming less energy means reducing the impact on the environment. 

In this context, an advanced energy management strategy represents a fundamental tool for effectively reducing the mismatch between the actual and the expected energy performance. 

The proposed approach combines traditional energy domain knowledge with IoT sensors data and novel machine learning techniques to build an effective daily decision-making system for energy management. In this framework, advanced solutions such as energy consumption and load forecasting, anomaly detection and diagnosis, and fault prognosis are implemented to obtain systematic energy saving. 

The research aims to bring a significant advancement of knowledge in the PNRR areas of interest, in particular with regard to Key Enabling Technologies.

In addition, the research has a strong transversal approach between topics related to artificial intelligence and those related to smart monitoring and energy management.


Erc Sector:

  • PE7_7 Signal processing
  • PE7_12 Electrical energy production, distribution, application
  • PE6_11 Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)


  • Energy management
  • Deep learning
  • Smart monitoring
  • Time series forecasting

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