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.
Highlights
Non-Intrusive Load Monitoring (NILM) techniques are under development, globally, as part of the effort to improve Electrical Energy Efficiency
Since the available NILM datasets did not match these requirements, we decided to pursue the development of a new dataset [4], named after our laboratory, by using an engineering development process starting with requirements elicitation
The subject of the NILM dataset can be placed in the broader area of energy-related datasets and the associated means of data sensing and recording
Summary
Douglas Paulo Bertrand Renaux 1, * , Fabiana Pottker 1 , Hellen Cristina Ancelmo 1 , André Eugenio Lazzaretti 1 , Carlos Raiumundo Erig Lima 1 , Robson Ribeiro Linhares 1 , Elder Oroski 1 , Lucas da Silva Nolasco 1 , Lucas Tokarski Lima 1 , Bruna Machado Mulinari 1 , José Reinaldo Lopes da Silva 1 , Júlio Shigeaki Omori 2 and Rodrigo Braun dos Santos 2. LIT-Laboratory of Innovation and Technology in Embedded Systems and Energy, Universidade Tecnológica. 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. 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.
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