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

LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica Federal do Paraná-UTFPR, Sete de Setembro, 3165, Curitiba 80230-901, Brazil
COPEL-Companhia Paranaense de Energia, José Izidoro Biazetto, 158, Curitiba 82305-100, Brazil
Author to whom correspondence should be addressed.
This paper is an extended and improved version of our paper published at the VIII Brazilian Symposium on Computing Systems Engineering (SBESC), Salvador, Brazil, 6–9 November 2018; pp. 243–249; 20th International Conference on Intelligent System Application to Power Systems (ISAP), New Delhi, India, 10–14 December 2019; pp. 1–7; 2019 IX Brazilian Symposium on Computing Systems Engineering (SBESC), Natal, Brazil, 19–22 November 2019; pp.1–8.
Energies 2020, 13(20), 5371;
Received: 21 August 2020 / Revised: 28 September 2020 / Accepted: 1 October 2020 / Published: 15 October 2020
(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)
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. View Full-Text
Keywords: Non-Intrusive Load Monitoring (NILM); NILM datasets; power signature; electric load simulation Non-Intrusive Load Monitoring (NILM); NILM datasets; power signature; electric load simulation
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MDPI and ACS Style

Renaux, D.P.B.; Pottker, F.; Ancelmo, H.C.; Lazzaretti, A.E.; Lima, C.R.E.; Linhares, R.R.; Oroski, E.; Nolasco, L.d.S.; Lima, L.T.; Mulinari, B.M.; Silva, J.R.L.d.; Omori, J.S.; Santos, R.B.d. A Dataset for Non-Intrusive Load Monitoring: Design and Implementation. Energies 2020, 13, 5371.

AMA Style

Renaux DPB, Pottker F, Ancelmo HC, Lazzaretti AE, Lima CRE, Linhares RR, Oroski E, Nolasco LdS, Lima LT, Mulinari BM, Silva JRLd, Omori JS, Santos RBd. A Dataset for Non-Intrusive Load Monitoring: Design and Implementation. Energies. 2020; 13(20):5371.

Chicago/Turabian Style

Renaux, Douglas P.B., Fabiana Pottker, Hellen C. Ancelmo, André E. Lazzaretti, Carlos R.E. Lima, Robson R. Linhares, Elder Oroski, Lucas d.S. Nolasco, Lucas T. Lima, Bruna M. Mulinari, José R.L.d. Silva, Júlio S. Omori, and Rodrigo B.d. Santos. 2020. "A Dataset for Non-Intrusive Load Monitoring: Design and Implementation" Energies 13, no. 20: 5371.

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