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

Paradiso, Francesca; Paganelli, Federica; Luchetta, Antonio; Giuli, Dino; Castrogiovanni, Pino

doi:10.1109/wowmom.2013.6583496

Cited by 26 publications

(32 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In most works, data were sampled up to 1 kHz [6,19], while in [13] and [24], the proposed appliance classification approaches were using samples gathered every 1 and 2 min, respectively.…”

Section: Low Sampling Ratementioning

confidence: 99%

Context-Based Energy Disaggregation in Smart Homes

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract:In this paper, we address the problem of energy conservation and optimization in residential environments by providing users with useful information to solicit a change in consumption behavior. Taking care to highly limit the costs of installation and management, our work proposes a Non-Intrusive Load Monitoring (NILM) approach, which consists of disaggregating the whole-house power consumption into the individual portions associated to each device. State of the art NILM algorithms need monitoring data sampled at high frequency, thus requiring high costs for data collection and management. In this paper, we propose an NILM approach that relaxes the requirements on monitoring data since it uses total active power measurements gathered at low frequency (about 1 Hz). The proposed approach is based on the use of Factorial Hidden Markov Models (FHMM) in conjunction with context information related to the user presence in the house and the hourly utilization of appliances. Through a set of tests, we investigated how the use of these additional context-awareness features could improve disaggregation results with respect to the basic FHMM algorithm. The tests have been performed by using Tracebase, an open dataset made of data gathered from real home environments.

show abstract

“…In most works, data were sampled up to 1 kHz [6,19], while in [13] and [24], the proposed appliance classification approaches were using samples gathered every 1 and 2 min, respectively.…”

Section: Low Sampling Ratementioning

confidence: 99%

Context-Based Energy Disaggregation in Smart Homes

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…It does not require user intervention. As a result, the proposed NILM, an extension of NILM, is different from the existing NILM methods presented in [6]- [11] and [18]- [21].…”

Section: Introductionmentioning

confidence: 89%

“…Such a smart house can be realized. However, high market price of smart appliances and noninteroperability among them are now barriers for the widespread adoption of smart appliances [6]. Instead, smart plugs are adopted in the transitional period.…”

Section: Introductionmentioning

confidence: 99%

“…Refer to [8] for more details on existing NILM methods. Existing NILM methods in [6]- [11] and [18]- [21] have explored and made an extensive effort on either feature extraction or load disaggregation, in order to address the difficulties in distinguishing household appliances with similar electrical features. However, it is acknowledged by most of the researchers that the traditional application of NILM has seen no great progress in recent years [21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Advanced Home Energy Management System Facilitated by Nonintrusive Load Monitoring With Automated Multiobjective Power Scheduling

Lin

Tsai

2015

IEEE Trans. Smart Grid

160

124

View full text Add to dashboard Cite

Nowadays, electricity energy demands requested from down-stream sectors in a smart grid constantly increase. One way to meet those demands is use of home energy management systems (HEMS). By effectively scheduling major household appliances in response to demand response (DR) schemes, residents can save their electricity bills. In this paper, an advanced HEMS facilitated by a nonintrusive load monitoring (NILM) technique with an automated nondominated sorting genetic algorithm-II (NSGA-II)-based multiobjective in-home power scheduling mechanism is proposed. The NILM as an electricity audit is able to nonintrusively estimate power consumed by each of monitored major household appliances at a certain period of time. Data identified by the NILM are very useful for DR implementation. For DR implementation, the NSGA-II-based multiobjective in-home power scheduling mechanism autonomously and meta-heuristically schedules monitored and enrolled major household appliances without user intervention. It is based on an analysis of the NILM with historical data with past trends. The experimental results reported in this paper reveal that the proposed advanced HEMS with the NILM assessed in a real-house environment with uncertainties is workable and feasible.Index Terms-Data fusion, demand response (DR), energy management system, ensemble learning, nonintrusive load monitoring (NILM), power scheduling, smart grid, smart house.

show abstract

“…Relying on multiple points of measurement, ILM is also referred to as multi-sensor metering [2]. Other authors are also using equivalent denominations such as Intrusive Appliance Load Monitoring systems (IALMs) [3][4], Hardware-Based Sub-Metering [5] [6] and Distributed System / Metering [7] [8]. In this paper we will use ILM as generic term.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Intrusive Load Monitoring for Appliance Recognition

Ridi

Gisler

Hennebert

2014

2014 22nd International Conference on Pattern Recognition

129

View full text Add to dashboard Cite

Electricity load monitoring of appliances has become an important task considering the recent economic and ecological trends. In this game, machine learning has an important part to play, allowing for energy consumption understanding, critical equipment monitoring and even human activity recognition. This paper provides a survey of current researches on Intrusive Load Monitoring (ILM) techniques. ILM relies on lowend electricity meter devices spread inside the habitations, as opposed to Non-Intrusive Load Monitoring (NILM) that relies on an unique point of measurement, the smart meter. Potential applications and principles of ILMs are presented and compared to NILM. A focus is also given on feature extraction and machine learning algorithms typically used for ILM applications.

show abstract

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

Cited by 26 publications

References 14 publications

Context-Based Energy Disaggregation in Smart Homes

Context-Based Energy Disaggregation in Smart Homes

An Advanced Home Energy Management System Facilitated by Nonintrusive Load Monitoring With Automated Multiobjective Power Scheduling

A Survey on Intrusive Load Monitoring for Appliance Recognition

Contact Info

Product

Resources

About