Pino Castrogiovanni

ANN-based appliance recognition from low-frequency energy monitoring data

The rational use and management of energy is a key objective for the evolution towards the smart grid. In particular in the private home domain the adoption of wide-scale energy consumption monitoring techniques can help end users in optimizing energy consumption behaviors. While most existing approaches for load disaggregation and classification requires high-frequency monitoring data, in this paper we propose an approach for detecting and identifying the appliances in use by analysing low-frequency monitoring data gathered by meters (i.e. smart plugs) distributed in the home. Our approach implements a supervised classification algorithm with artificial neural networks and has been tested with a dataset of power traces collected in real-world home settings.

Appliance Recognition in an OSGi-Based Home Energy Management Gateway

The rational use and management of energy is considered a key societal and technological challenge. Home energy management systems (HEMS) have been introduced especially in private home domains to support users in managing and controlling energy consuming devices. Recent studies have shown that informing users about their habits with appliances as well as their usage pattern can help to achieve energy reduction in private households. This requires instruments able to monitor energy consumption at fine grain level and provide this information to consumers. While the most existing approaches for load disaggregation and classification require high-frequency monitoring data, in this paper we propose an approach that exploits low-frequency monitoring data gathered by meters (i.e., Smart Plugs) displaced in the home. Moreover, while the most existing works dealing with appliance classification delegate the classification task to a remote central server, we propose a distributed approach where data processing and appliance recognition are performed locally in the Home Gateway. Our approach is based on a distributed load monitoring system made of Smart Plugs attached to devices and connected to a Home Gateway via the ZigBee protocol. The Home Gateway is based on the OSGi platform, collects data from home devices, and hosts both data processing and user interaction logic.

Transparent Bandwidth Aggregation for Residential Access Networks

We propose, implement, and evaluate a bandwidth aggregation service for residential users that allows to improve the upload throughput of the asymmetric digital subscriber line connection by leveraging the unused bandwidth of neighboring users. The residential access gateway adopts the 802.11 radio interface to simultaneously serve the local home users and to share the broadband connectivity with neighboring access gateways. Differently from previous works, our aggregation scheme is transparent both for local users, who are not required to modify their applications or device drivers, and for neighboring users, who do not experience any meaningful performance degradation. To evaluate the achievable performance and tune the parameters driving the traffic balancing, we developed a fluid model that was shown experimentally to be very accurate. Our proposed scheme is amenable to efficient implementation on Linux networking stack. Indeed, we implemented it and tested in some realistic scenarios, showing an efficient exploitation of the whole available bandwidth, also for legacy cloud storage applications.