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Energies | Vol.13, Issue.20 | 2020-10-15 | Pages

Energies

A Dataset for Non-Intrusive Load Monitoring: Design and Implementation †

Oroski,    Mulinari, Bruna Machado   Ancelmo, Hellen Cristina   Lima, Carlos Raiumundo Erig   Omori, Júlio Shigeaki   da Silva, José Reinaldo Lopes   Lima, Lucas Tokarski   Pottker, Fabiana   dos Santos, Rodrigo Braun   da Silva Nolasco, Lucas   Renaux, Douglas Paulo Bertrand   Linhares, Robson Ribeiro   Lazzaretti, André Eugenio  
Abstract

A NILM dataset is a valuable tool in the development of Non-Intrusive Load Monitoring techniques, as it provides a means of evaluation of novel techniques and algorithms, as well as for benchmarking. The figure of merit of a NILM dataset includes characteristics such as the sampling frequency of the voltage, current, or power, the availability of indications (ground-truth) of load events during recording, the variety and representativeness of the loads, and the variety of situations these loads are subject to. Considering such aspects, the proposed LIT-Dataset was designed, populated, evaluated, and made publicly available to support NILM development. Among the distinct features of the LIT-Dataset is the labeling of the load events at sample level resolution and with an accuracy and precision better than 5 ms. The availability of such precise timing information, which also includes the identification of the load and the sort of power event, is an essential requirement both for the evaluation of NILM algorithms and techniques, as well as for the training of NILM systems, particularly those based on Machine Learning.

Original Text (This is the original text for your reference.)

A Dataset for Non-Intrusive Load Monitoring: Design and Implementation †

A NILM dataset is a valuable tool in the development of Non-Intrusive Load Monitoring techniques, as it provides a means of evaluation of novel techniques and algorithms, as well as for benchmarking. The figure of merit of a NILM dataset includes characteristics such as the sampling frequency of the voltage, current, or power, the availability of indications (ground-truth) of load events during recording, the variety and representativeness of the loads, and the variety of situations these loads are subject to. Considering such aspects, the proposed LIT-Dataset was designed, populated, evaluated, and made publicly available to support NILM development. Among the distinct features of the LIT-Dataset is the labeling of the load events at sample level resolution and with an accuracy and precision better than 5 ms. The availability of such precise timing information, which also includes the identification of the load and the sort of power event, is an essential requirement both for the evaluation of NILM algorithms and techniques, as well as for the training of NILM systems, particularly those based on Machine Learning.

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Keywords

Learning Dataset NILM

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Oroski, , Mulinari, Bruna Machado, Ancelmo, Hellen Cristina, Lima, Carlos Raiumundo Erig, Omori, Júlio Shigeaki, da Silva, José Reinaldo Lopes, Lima, Lucas Tokarski, Pottker, Fabiana, dos Santos, Rodrigo Braun, da Silva Nolasco, Lucas,Renaux, Douglas Paulo Bertrand, Linhares, Robson Ribeiro, Lazzaretti, André Eugenio,.A Dataset for Non-Intrusive Load Monitoring: Design and Implementation †. 13 (20),.

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