Data Requirements for Applying Machine Learning to Energy Disaggregation

Changho Shin, Seungeun Rho, Hyoseop Lee and Wonjong Rhee
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Changho Shin: Encored Technologies; Seoul 06109, Korea
Seungeun Rho: NC Soft Inc., Seongnam 13494, Korea
Hyoseop Lee: Encored Technologies; Seoul 06109, Korea
Wonjong Rhee: Department of Transdisciplinary Studies, Seoul National University, Seoul 08826, Korea

Energies, 2019, vol. 12, issue 9, 1-19

Abstract: Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment have been rather limited, possibly because the commonly used datasets are not adequate for NILM research. In this study, we report the findings from a newly collected dataset that contains 10 Hz sampling data for 58 houses. The dataset not only contains the aggregate measurements, but also individual appliance measurements for three types of appliances. By applying three classification algorithms (vanilla DNN (Deep Neural Network), ML (Machine Learning) with feature engineering, and CNN (Convolutional Neural Network) with hyper-parameter tuning) and a recent regression algorithm (Subtask Gated Network) to the new dataset, we show that NILM performance can be significantly limited when the data sampling rate is too low or when the number of distinct houses in the dataset is too small. The well-known NILM datasets that are popular in the research community do not meet these requirements. Our results indicate that higher quality datasets should be used to expedite the progress of NILM research.

Keywords: energy disaggregation; nonintrusive load monitoring (NILM); machine learning; data requirements (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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