フォロー
Christoph Klemenjak
Christoph Klemenjak
Research Assistant @ University of Klagenfurt
確認したメール アドレス: ieee.org - ホームページ
タイトル
引用先
引用先
A synthetic energy dataset for non-intrusive load monitoring in households
C Klemenjak, C Kovatsch, M Herold, W Elmenreich
Scientific data 7 (1), 108, 2020
1072020
Adaptive weighted recurrence graphs for appliance recognition in non-intrusive load monitoring
A Faustine, L Pereira, C Klemenjak
IEEE Transactions on Smart Grid 12 (1), 398-406, 2020
962020
Non-intrusive load monitoring: A review and outlook
C Klemenjak, P Goldsborough
arXiv preprint arXiv:1610.01191, 2016
822016
Towards comparability in non-intrusive load monitoring: On data and performance evaluation
C Klemenjak, S Makonin, W Elmenreich
2020 IEEE power & energy society innovative smart grid technologies …, 2020
602020
YoMo: the Arduino-based smart metering board
C Klemenjak, D Egarter, W Elmenreich
Computer Science-Research and Development 31, 97-103, 2016
562016
Electricity consumption data sets: Pitfalls and opportunities
C Klemenjak, A Reinhardt, L Pereira, S Makonin, M Bergés, ...
Proceedings of the 6th ACM international conference on systems for energy …, 2019
412019
How does load disaggregation performance depend on data characteristics? insights from a benchmarking study
A Reinhardt, C Klemenjak
Proceedings of the eleventh ACM international conference on future energy …, 2020
352020
Augmenting an assisted living lab with non-intrusive load monitoring
H Bousbiat, C Klemenjak, G Leitner, W Elmenreich
2020 IEEE international instrumentation and measurement technology …, 2020
252020
On metrics to assess the transferability of machine learning models in non-intrusive load monitoring
C Klemenjak, A Faustine, S Makonin, W Elmenreich
arXiv preprint arXiv:1912.06200, 2019
212019
Exploring time series imaging for load disaggregation
H Bousbiat, C Klemenjak, W Elmenreich
Proceedings of the 7th ACM International Conference on Systems for Energy …, 2020
192020
YoMoPie: A User-Oriented Energy Monitor to Enhance Energy Efficiency in Households
C Klemenjak, S Jost, W Elmenreich
2018 IEEE Conference on Technologies for Sustainability (SusTech), 7, 0
16*
Investigating the performance gap between testing on real and denoised aggregates in non-intrusive load monitoring
C Klemenjak, S Makonin, W Elmenreich
Energy Informatics 4, 1-15, 2021
112021
Unlocking the Full Potential of Neural NILM: On Automation, Hyperparameters, and Modular Pipelines
H Bousbiat, A Faustine, C Klemenjak, L Pereira, W Elmenreich
IEEE Transactions on Industrial Informatics 19 (5), 7002-7010, 2022
82022
Device-free user activity detection using non-intrusive load monitoring: a case study
A Reinhardt, C Klemenjak
Proceedings of the 2nd ACM Workshop on Device-Free Human Sensing, 1-5, 2020
82020
2020 IEEE power & energy society innovative smart grid technologies conference (ISGT)
C Klemenjak, S Makonin, W Elmenreich
IEEE, 2020
72020
On the Applicability of Correlation Filters for Appliance Detection in Smart Meter Readings
C Klemenjak, W Elmenreich
2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2018
72018
On metrics to assess the transferability of machine learning models in non-intrusive load monitoring. arXiv 2019
C Klemenjak, A Faustine, S Makonin, W Elmenreich
arXiv preprint arXiv:1912.06200, 0
7
Stop: Exploring bayesian surprise to better train nilm
R Jones, C Klemenjak, S Makonin, IV Bajić
Proceedings of the 5th International Workshop on Non-Intrusive Load …, 2020
62020
Exploring Bayesian surprise to prevent overfitting and to predict model performance in non-intrusive load monitoring
R Jones, C Klemenjak, S Makonin, IV Bajic
arXiv preprint arXiv:2009.07756, 2020
62020
On metrics to assess the transferability of machine learning models in non-intrusive load monitoring. arXiv
C Klemenjak, A Faustine, S Makonin, W Elmenreich
arXiv preprint arXiv:1912.06200, 2019
52019
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