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arXiv · 1910.08149

Multi Label Restricted Boltzmann Machine for Non-Intrusive Load Monitoring

Sagar Verma·Shikha Singh·Angshul Majumdar
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Abstract

Increasing population indicates that energy demands need to be managed in the residential sector. Prior studies have reflected that the customers tend to reduce a significant amount of energy consumption if they are provided with appliance-level feedback. This observation has increased the relevance of load monitoring in today's tech-savvy world. Most of the previously proposed solutions claim to perform load monitoring without intrusion, but they are not completely non-intrusive. These methods require historical appliance-level data for training the model for each of the devices. This data is gathered by putting a sensor on each of the appliances present in the home which causes intrusion in the building. Some recent studies have proposed that if we frame Non-Intrusive Load Monitoring (NILM) as a multi-label classification problem, the need for appliance-level data can be avoided. In this paper, we propose Multi-label Restricted Boltzmann Machine(ML-RBM) for NILM and report an experimental evaluation of proposed and state-of-the-art techniques.

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Sagar Verma, Shikha Singh, Angshul Majumdar. 2019-10-17. Multi Label Restricted Boltzmann Machine for Non-Intrusive Load Monitoring. https://arxiv.org/abs/1910.08149

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