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

**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**


Received: 21 August 2020; Accepted: 1 October 2020; Published: 15 October 2020

**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.

**Keywords:** Non-Intrusive Load Monitoring (NILM); NILM datasets; power signature; electric load simulation
