Abstract
Non-Intrusive Load Monitoring (NILM) is a set of techniques to gain deep insights into workflows inside buildings based on data provided by smart meters. In this way, the combined consumption needs only to be monitored at a single, central point in the household, providing advantages such as reduced costs for metering equipment. Over the years, a plethora of load monitoring algorithms has been proposed comprising approaches based on Hidden Markov Models (HMM), algorithms based on combinatorial optimisation, and more recently, approaches based on machine learning. However, reproducibility, comparability, and performance evaluation remain open research issues since there is no standardised way researchers evaluate their approaches and report performance. In this paper, the author points out open research issues of performance evaluation in NILM, presents a short survey of deep learning approaches for NILM, and formulates research questions related to open issues in NILM. An outline of future work is given including applied methodology and expected findings.
Highlights
A plethora of load monitoring algorithms has been proposed comprising approaches based on Hidden Markov Models (HMM), algorithms based on combinatorial optimisation, and more recently, approaches based on machine learning
The author points out open research issues of performance evaluation in Non-Intrusive Load Monitoring (NILM), presents a short survey of deep learning approaches for NILM, and formulates research questions related to open issues in NILM
With regard to recent suggestions of related work for improved comparability in NILM, the authors identify the need for an extensive comparison case study that considers well-established NILM algorithms based on Hidden Markov Models as well as novel machine learning approaches based on deep neural networks
Summary
Neither a comparison case study evaluating the generalisation abilities of existing NILM algorithms was conducted nor a machine-learning NILM algorithm was developed that shows acceptable performance on unseen smart meter data. The author highlights open research issues of performance evaluation in Non-Intrusive Load Monitoring (NILM), presents a short survey of deep learning approaches for NILM, formulates research questions related to the presented research problems, and gives an outline of future work.
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