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 widescale 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

DOI: 10.1109/WoWMoM.2013.6583496

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@inproceedings{Paradiso2013ANNbasedAR, title={ANN-based appliance recognition from low-frequency energy monitoring data}, author={Francesca Paradiso and Federica Paganelli and Antonio Luchetta and Dino Giuli and Pino Castrogiovanni}, booktitle={WOWMOM}, year={2013} }