Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques

Cristina Puente,Yolanda González-Arechavala,Eugenio Francisco Sánchez-Úbeda,Rafael Palacios

doi:10.3390/en13123117

Cristina Puente, Yolanda González-Arechavala + Show 2 more

Open Access

https://doi.org/10.3390/en13123117

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Journal: Energies	Publication Date: Jun 16, 2020
Citations: 24	License type: CC BY 4.0

Affiliation: Comillas Pontifical University

Abstract

Non-intrusive load monitoring (NILM) has become an important subject of study, since it provides benefits to both consumers and utility companies. The analysis of smart meter signals is useful for identifying consumption patterns and user behaviors, in order to make predictions and optimizations to anticipate the use of electrical appliances at home. However, the problem with this kind of analysis rests in how to isolate individual appliances from an aggregated consumption signal. In this work, we propose an unsupervised disaggregation method based on a controlled dataset obtained using smart meters in a standard household. By using soft computing techniques, the proposed methodology can identify the behavior of each of the devices from aggregated consumption records. In the approach developed in this work, it is possible to detect changes in power levels and to build a box model, consisting of a sequence of rectangles of different heights (power) and widths (time), which is highly adaptable to the real-life working conditions of household appliances. The system was developed and tested using data collected at households in France and the UK (UK-domestic appliance-level electricity (DALE) dataset). The proposed analysis method serves as a basis to be applied to large amounts of data collected by distribution companies with smart meters.

Highlights

Two of the main global problems that we are currently facing are pollution and consumption control
As an example of the effectiveness of the proposed methodology for detecting the use of electrical appliances, Figure 14 shows a challenging scenario in which several appliances are operating simultaneously, making the resulting total household power signal difficult to analyze
Since the dataset used for this work uses smart meters for different sections of the house, it is possible to understand the shape of the total power by looking at the decomposed signals in subplots 1 through 3

Summary

Introduction

Two of the main global problems that we are currently facing are pollution and consumption control. In the Paris COP (United Nations Climate Change Conference 2015), the United Nations agreed to limit global warming to 1.5 degrees by 2100 and, reducing energy consumption has become a key task for achieving this goal [1]. The fact that 27% of electricity consumption in Europe is attributed to households emphasizes the need to enact regulations promoting suitable and responsible electricity usage. In this sense, monitoring home energy consumption is an important task in order to optimize and reduce electricity usage [2,3].

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