A Generative Model for Non-Intrusive Load Monitoring in Commercial Buildings

TL;DR

This paper introduces a generative model for creating synthetic electrical load data in commercial buildings, addressing data scarcity and complexity issues in non-intrusive load monitoring (NILM) research.

Contribution

The study presents a statistical analysis of commercial building signals, develops a realistic synthetic data generator, and releases a dataset to support NILM research in large buildings.

Findings

Commercial and residential signals differ significantly.

Synthetic data closely matches real measurements.

The dataset facilitates NILM algorithm development.

Abstract

In the recent years, there has been an increasing academic and industrial interest for analyzing the electrical consumption of commercial buildings. Whilst having similarities with the Non Intrusive Load Monitoring (NILM) tasks for residential buildings, the nature of the signals that are collected from large commercial buildings introduces additional difficulties to the NILM research causing existing NILM approaches to fail. On the other hand, the amount of publicly available datasets collected from commercial buildings is very limited, which makes the NILM research even more challenging for this type of large buildings. In this study, we aim at addressing these issues. We first present an extensive statistical analysis of both commercial and residential measurements from public and private datasets and show important differences. Secondly, we develop an algorithm for generating synthetic current waveforms. We then demonstrate using real measurement and quantitative metrics that both our device model and our simulations are realistic and can be used to evaluate NILM algorithms. Finally, to encourage research on commercial buildings we release a synthesized dataset.