Increasing energy efficiency is a key topic in smart cities management. To this aim, Non-Intrusive Appliance Load Monitoring (NIALM) has a crucial role in smart infrastructures for reducing power consumption and, hence, improving energy saving. Combining Internet of Things (IoT) and Artificial intelligence (AI) can significantly support NIALM activities, promoting the development of next-generation Cognitive Smart Meters (CSMs). CSMs allow better tracking of power consumption and generation, and can be used to accomplish reliable transmission of monitored data through wireless communication infrastructures in a smart environment. In this paper, we present the development of a cost-effective NIALM infrastructure exploiting IoT features and AI solutions. Specifically, the proposed infrastructure involves IoT-based CSMs and an Edge-based Accumulator that collects CSMs transmitted data and extracts the features necessary to train an on-board Machine Learning (ML) model with limited computational requirements to minimize costs and latency. We performed initial evaluations of the proposed solution to demonstrate the goodness of the approach and of the used ML model.

Intelligent IoT for non-intrusive appliance load monitoring infrastructures in smart cities

Buzachis A.
Primo
;
Fazio M.
Secondo
;
Galletta A.;Celesti A.
Penultimo
;
Villari M.
Ultimo
2019-01-01

Abstract

Increasing energy efficiency is a key topic in smart cities management. To this aim, Non-Intrusive Appliance Load Monitoring (NIALM) has a crucial role in smart infrastructures for reducing power consumption and, hence, improving energy saving. Combining Internet of Things (IoT) and Artificial intelligence (AI) can significantly support NIALM activities, promoting the development of next-generation Cognitive Smart Meters (CSMs). CSMs allow better tracking of power consumption and generation, and can be used to accomplish reliable transmission of monitored data through wireless communication infrastructures in a smart environment. In this paper, we present the development of a cost-effective NIALM infrastructure exploiting IoT features and AI solutions. Specifically, the proposed infrastructure involves IoT-based CSMs and an Edge-based Accumulator that collects CSMs transmitted data and extracts the features necessary to train an on-board Machine Learning (ML) model with limited computational requirements to minimize costs and latency. We performed initial evaluations of the proposed solution to demonstrate the goodness of the approach and of the used ML model.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3185412
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