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Open collaborative smart plugs for energy management.

Almir Neto1, Luis Gomes2, Zita Vale2

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Summary

A new smart plug, the Environmental Awareness smart Plug (EnAPlug), uses Tiny Machine Learning (Tiny ML) to predict refrigerator humidity and motor activity. This enables enhanced energy management through local data storage and smart home automation.

Keywords:
Edge ImpulseEnergy analyzerInternet of ThingsMachine LearningSmart plugs

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Area of Science:

  • Domotics and Home Automation
  • Machine Learning Applications
  • Energy Management Systems

Background:

  • The proliferation of smart devices in home automation necessitates integrated energy management.
  • Understanding the interplay between environmental and energy parameters is crucial for efficient device operation.
  • Existing smart plugs lack local data storage capabilities for advanced analysis.

Purpose of the Study:

  • To propose an Environmental Awareness smart Plug (EnAPlug) integrating machine learning for environmental awareness.
  • To demonstrate EnAPlug's capability in predicting refrigerator internal humidity and motor activation.
  • To explore the potential for enhanced energy management through localized data processing.

Main Methods:

  • Development of an Environmental Awareness smart Plug (EnAPlug) utilizing Tiny Machine Learning (Tiny ML).
  • Application of EnAPlug to a refrigerator for real-time environmental and operational data collection.
  • Implementation of predictive models for internal humidity and motor activation (5-minute ahead prediction).

Main Results:

  • Humidity prediction models achieved a Root Mean Squared Error (RMSE) of 0.055 and 0.058, with R2 scores of 0.97 and 0.99.
  • Motor activation prediction models demonstrated high accuracy (94.74%-94.84%) and F1 scores (0.93-0.97).
  • The EnAPlug prototype successfully enabled local data storage, differentiating it from conventional smart plugs.

Conclusions:

  • The EnAPlug prototype shows promising results for environmental parameter prediction and operational forecasting.
  • Local data storage capability enhances the potential for sophisticated, on-device energy management strategies.
  • The integration of Tiny ML in smart plugs offers a pathway to more intelligent and efficient home automation.