GREEND: An energy consumption dataset of households in Italy and Austria

Monacchi, Andrea; Egarter, Dominik; Elmenreich, Wilfried; D'Alessandro, Salvatore; Tonello, Andrea M.

doi:10.1109/smartgridcomm.2014.7007698

Cited by 149 publications

(76 citation statements)

References 19 publications

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“…The REDD [23] data set, for instance, was published by Kolter and Johnson in 2011. Since then, other data sets have been published such as the Smart* data set [4], GREEND [26], BLUED [1], AMPds [24], UK-Dale [18], iAWE [5], and the Pecan Street [15] data set.…”

Section: The Eco Data Setmentioning

confidence: 99%

The ECO data set and the performance of non-intrusive load monitoring algorithms

Beckel

Kleiminger

Cicchetti

et al. 2014

Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings

268

157

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Non-intrusive load monitoring (NILM) is a popular approach to estimate appliance-level electricity consumption from aggregate consumption data of households. Assessing the suitability of NILM algorithms to be used in real scenarios is however still cumbersome, mainly because there exists no standardized evaluation procedure for NILM algorithms and the availability of comprehensive electricity consumption data sets on which to run such a procedure is still limited. This paper contributes to the solution of this problem by: (1) outlining the key dimensions of the design space of NILM algorithms; (2) presenting a novel, comprehensive data set to evaluate the performance of NILM algorithms; (3) describing the design and implementation of a framework that significantly eases the evaluation of NILM algorithms using different data sets and parameter configurations; (4) demonstrating the use of the presented framework and data set through an extensive performance evaluation of four selected NILM algorithms. Both the presented data set and the evaluation framework are made publicly available.

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Section: The Eco Data Setmentioning

confidence: 99%

The ECO data set and the performance of non-intrusive load monitoring algorithms

Beckel

Kleiminger

Cicchetti

et al. 2014

Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings

268

157

View full text Add to dashboard Cite

show abstract

“…The following step was to carry out a measurement campaign in selected households to investigate actual energy utilization. In particular, we have been monitoring the following scenarios: The main outcome was the GREEND dataset, containing power profiles of selected devices (see Table 1) at 1 Hz resolution for a time span of about 1 year [20].…”

Section: Measurement Campaignmentioning

confidence: 99%

An open solution to provide personalized feedback for building energy management

Monacchi

Versolatto²,

Herold

et al. 2017

AIS

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The integration of renewable energy sources increases the complexity in mantaining the power grid. In particular, the highly dynamic nature of generation and consumption demands for a better utilization of energy resources, which seen the cost of storage infrastructure, can only be achieved through demand-response. Accordingly, the availability of energy and potential overload situations can be reflected using a price signal. The effectiveness of this mechanism arises from the flexibility of device operation, which is nevertheless heavily reliant on the exchange of information between the grid and its consumers. In this paper, we investigate the capability of an interactive energy management system to timely inform users on energy usage, in order to promote an optimal use of local resources. In particular, we analyze data being collected in several households in Italy and Austria to gain insights into usage behavior and drive the design of more effective systems. The outcome is the formulation of energy efficiency policies for residential buildings, as well as the design of an energy management system, consisting of hardware measurement units and a management software. The Mjölnir framework, which we release for open use, provides a platform where various feedback concepts can be implemented and assessed. This * A. Monacchi, M. Herold, D. Egarter and W. Elmenreich are with the Institute of Networked and Embedded Systems, Alpen-Adria-Universität Klagenfurt, F. Versolatto and A. M. Tonello are with WiTiKee Srl 1 arXiv:1505.01311v1 [cs.HC] 6 May 2015includes widgets displaying disaggregated and aggregated consumption information, as well as daily production and tailored advices. The formulated policies were implemented as an advisor widget able to autonomously analyze usage and provide tailored energy feedback.

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“…Later that year, the ECO data set [7] was released, which contains 1 second data for six houses in Switzerland. More recently, the GREEND data set [10] was released, which contains 1 second appliance data from nine houses in Italy and Austria. Most recently, the SustData data set [11] and DRED data set [12] have been released containing data from 50 and 1 home respectively.…”

Section: Existing Data Setsmentioning

confidence: 99%

Dataport and NILMTK: A building data set designed for non-intrusive load monitoring

Parson

Fisher

Hersey

et al. 2015

2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP)

107

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Abstract-Non-intrusive load monitoring (NILM), or energy disaggregation, is the process of using signal processing and machine learning to separate the energy consumption of a building into individual appliances. In recent years, a number of data sets have been released in order to evaluate such approaches, which contain both building-level and appliance-level energy data. However, these data sets typically cover less than 10 households due to the financial cost of such deployments, and are not released in a format which allows the data sets to be easily used by energy disaggregation researchers. To this end, the Dataport database was created by Pecan Street Inc, which contains 1 minute circuit-level and building-level electricity data from 722 households. Furthermore, the non-intrusive load monitoring toolkit (NILMTK) was released in 2014, which provides software infrastructure to support energy disaggregation research, such as data set parsers, benchmark disaggregation algorithms and accuracy metrics. This paper describes the release of a subset of the Dataport database in NILMTK format, containing one month of electricity data from 669 households. Through the release of this Dataport data in NILMTK format, we pose a challenge to the signal processing community to produce energy disaggregation algorithms which are both accurate and scalable.

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GREEND: An energy consumption dataset of households in Italy and Austria

Cited by 149 publications

References 19 publications

The ECO data set and the performance of non-intrusive load monitoring algorithms

The ECO data set and the performance of non-intrusive load monitoring algorithms

An open solution to provide personalized feedback for building energy management

Dataport and NILMTK: A building data set designed for non-intrusive load monitoring

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