Applications of NILM in the Optimization Management of Intelligent Home Energy Management System

Yan, Qingdong; Wang, Xudong; Wei, Zun

doi:10.1109/ispec50848.2020.9351198

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…First of all, this kind of information can significantly increment the awareness of end-users to reduce the power consumption of their electrical devices. Indeed, it has been demonstrated that end-users can save up to 12% of their energy consumption when provided with sufficiently detailed energy feedback (Yan et al 2020). Those savings can be achieved by reducing the use of more energy-intensive devices or by replacing obsolete and inefficient devices like fridges and freezers.…”

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confidence: 99%

Non-intrusive load monitoring techniques for the disaggregation of ON/OFF appliances

et al. 2022

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Nowadays, Non-Intrusive Load Monitoring techniques are sufficiently accurate to provide valuable insights to the end-users and improve their electricity behaviours. Indeed, previous works show that commonly used appliances (fridge, dishwasher, washing machine) can be easily disaggregated thanks to their abundance of electrical features. Nevertheless, there are still many ON/OFF devices (e.g. heaters, kettles, air conditioners, hair dryers) that present very poor power signatures, preventing their disaggregation with traditional algorithms. In this work, we propose a new online clustering method exploiting both operational features (peak power, duration) and external features (time of use, day of week, weekday/weekend) in order to recognize ON/OFF devices. The proposed algorithm is intended to support an existing disaggregation algorithm that is already able to classify at least 80% of the total energy consumption of the house. Thanks to our approach, we improved the performance of our existing disaggreation algorithm from 80% to 87% of the total energy consumption in the monitored houses. In particular, we found that 85% of the clusters were identified by only using operational features, while external features allowed us to identify the remaining 15% of the clusters. The algorithm needs to collect on average less than 40 operations to find a cluster, which demonstrates its applicability in the real world.

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confidence: 99%