# Appliance Operation Modes Identification Using Cycles Clustering

arXiv: https://arxiv.org/abs/2101.10472 · HTML: https://mlsanity.com/paper/2101.10472/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 4 of 4)

- [Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges](https://mlsanity.com/paper/1802.04117/) (2018)
- [A Distributed Online Pricing Strategy for Demand Response Programs](https://mlsanity.com/paper/1702.05551/) (2017)
- [Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature](https://mlsanity.com/paper/1605.00962/) (2016)
- [Demand Response For Residential Uses: A Data Analytics Approach](https://mlsanity.com/paper/2008.02908/) (2020)

## Abstract (arXiv)

The increasing cost, energy demand, and environmental issues has led many researchers to find approaches for energy monitoring, and hence energy conservation. The emerging technologies of Internet of Things (IoT) and Machine Learning (ML) deliver techniques that have the potential to efficiently conserve energy and improve the utilization of energy consumption. Smart Home Energy Management Systems (SHEMSs) have the potential to contribute in energy conservation through the application of Demand Response (DR) in the residential sector. In this paper, we propose appliances Operation Modes Identification using Cycles Clustering (OMICC) which is SHEMS fundamental approach that utilizes the sensed residential disaggregated power consumption in supporting DR by providing consumers the opportunity to select lighter appliance operation modes. The cycles of the Single Usage Profile (SUP) of an appliance are extracted and reformed into features in terms of clusters of cycles. These features are then used to identify the operation mode used in every occurrence using K-Nearest Neighbors (KNN). Operation modes identification is considered a basis for many potential smart DR applications within SHEMS towards the consumers or the suppliers