Electrical Appliance Classification using Deep Convolutional Neural Networks on High Frequency Current Measurements. - Daniel Jorde; Thomas Kriechbaumer; Hans-Arno Jacobsen (SmartGridComm, 2018)

Appliance Event Detection - A Multivariate, Supervised Classification Approach. - Matthias Kahl; Thomas Kriechbaumer; Daniel Jorde; Anwar Ul Haq; Hans-Arno Jacobsen (e-Energy, 2019)

Waveform Signal Entropy and Compression Study of Whole-Building Energy Datasets. - Thomas Kriechbaumer; Daniel Jorde; Hans-Arno Jacobsen (e-Energy, 2019)

MEED: An Unsupervised Multi-Environment Event Detector for Non-Intrusive Load Monitoring. - Daniel Jorde; Matthias Kahl; Hans-Arno Jacobsen (SmartGridComm, 2019)

The DEBS 2020 grand challenge. - Vincenzo Gulisano; Daniel Jorde; Ruben Mayer; Hannaneh Najdataei; Dimitris Palyvos-Giannas (DEBS, 2020)

BERT4NILM: A Bidirectional Transformer Model for Non-Intrusive Load Monitoring. - Zhenrui Yue; Camilo Requena Witzig; Daniel Jorde; Hans-Arno Jacobsen (NILM@SenSys, 2020)

Event Detection for Energy Consumption Monitoring. - Daniel Jorde; Hans-Arno Jacobsen (IEEE Trans. Sustain. Comput., 2021)

Learning from Power: Machine Learning on Electrical Signals. - Daniel Jorde (2021)

Representation Learning for Appliance Recognition: A Comparison to Classical Machine Learning. - Matthias Kahl; Daniel Jorde; Hans-Arno Jacobsen (CoRR, 2022)