Implementing Scikit-Learn libralies for developing a Non-Intrusive Load Monitoring program

Abstract

In recent year, loads monitoring play an important role in energy saving and development smart grid. However, detailed monitoring of individ-ual loads requires a huge number of measuring devices, leading to a lot of difficulties in investment, development and management. To solve this problem, non-intrusive load monitoring technique which apply machine learning algorithms allows to identify individual loads consump-tions base on total consumption datas, thereby significantly reducing the number of measuring devices and investment costs. In this paper, through the application of machine learning algorithms, research team analyzed consumption data set of a apartment with total and detailed consumption datas. The result point outs difficulties and potentials of applied machine learning in the energy disaggregation.