Dear all,
last week, Christoph Klemenjak presented our research paper on Energy Consumption Datasets at ACM BuildSys. The paper is the outcome of a collaboration with NILM experts and discusses pitfalls and opportunities with regard to future measurement campaigns.
Abstract
Real-world data sets are crucial to develop and test signal processing and machine learning algorithms to solve energy-related problems.
Their scope and data resolution is, however, often limited to the means required to fulfill the experimenters' objectives and moreover governed by personal experience, budgetary and time constraints, and the availability of equipment.
As a result, numerous differences between data sets can be observed, e.g., regarding their sampling rates, the number of sensors deployed, their amplitude resolutions, storage formats, or the availability and extent of ground-truth annotations.
This heterogeneity poses a significant problem for researchers intending to comparatively use data sets because of the required data conversion, re-sampling, and adaptation steps.
In short, there is a lack of widely agreed best practices for designing, deploying, and operating electrical data collection systems.
We address this limitation by dissecting the collection methodologies used in existing data sets.
By offering recommendations for data collection, data storage, and data provision, we intend to foster the creation of data sets with increased usability and comparability, and thus a greater benefit to the community.
Find the paper here.
Please direct feedback to klemenjak@ieee.org
Have a great day,
Christoph
Tuesday, November 19, 2019
New NILM paper on Comparability!
Dear all,
we proudly announce our latest NILM paper on comparability in NILM scholarship. In this paper, we discuss data noise, appliance events as well as performance evaluation in general. The paper is to be presented at 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) in Washington DC.
Abstract
Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques that provide insights into the energy consumption of households and industrial facilities. Latest contributions show significant improvements in terms of accuracy and generalisation abilities. Despite all progress made concerning disaggregation techniques, performance evaluation and comparability remains an open research question. The lack of standardisation and consensus on evaluation procedures makes reproducibility and comparability extremely difficult.
In this paper, we draw attention to comparability in NILM with a focus on highlighting the considerable differences amongst common energy datasets used to test the performance of algorithms. We divide discussion on comparability into data aspects, performance metrics, and give a close view on evaluation processes. Detailed information on pre-processing as well as data cleaning methods, the importance of unified performance reporting, and the need for complexity measures in load disaggregation are found to be the most urgent issues in NILM-related research. In addition, our evaluation suggests that datasets should be chosen carefully. We conclude by formulating suggestions for future work to enhance comparability.
Get the paper here. - Explore supplemental material here.
Please direct feedback and further comments to klemenjak@ieee.org
Have a great day,
Christoph
Subscribe to:
Posts (Atom)