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Found 12 papers, 1 papers with code
Towards Data-Driven Electricity Management: Multi-Region Harmonized Data and Knowledge Graph
Due to growing population and technological advances, global electricity consumption, and consequently also CO2 emissions are increasing.
Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach
This paper showcases two real-world use cases (home appliances classification and airport shuttle demand prediction) where a hierarchical model applied in the context of zero-inflated data leads to excellent results.
Deep Feature Learning for Wireless Spectrum Data
In recent years, the traditional feature engineering process for training machine learning models is being automated by the feature extraction layers integrated in deep learning architectures.
Energy Efficient Deep Multi-Label ON/OFF Classification of Low Frequency Metered Home Appliances
We also show a 12 percentage point performance advantage of the proposed DL based model over a random forest model and observe performance degradation with the increase of the number of devices in the household, namely with each additional 5 devices, the average performance degrades by approximately 7 percentage points.
XAI for Self-supervised Clustering of Wireless Spectrum Activity
In this paper, we propose a methodology for explaining deep clustering, self-supervised learning architectures comprised of a representation learning part based on a Convolutional Neural Network (CNN) and a clustering part.
Resource-aware Deep Learning for Wireless Fingerprinting Localization
Location based services, already popular with end users, are now inevitably becoming part of new wireless infrastructures and emerging business processes.
Self-supervised Learning for Clustering of Wireless Spectrum Activity
In particular, we compare the performance of two SSL models, one based on a reference DeepCluster architecture and one adapted for spectrum activity identification and clustering, and a baseline model based on K-means clustering algorithm.
On Designing Data Models for Energy Feature Stores
We first propose a taxonomy for designing data models suitable for energy applications, explain how this model can support the design of features and their subsequent management by specialized feature stores.
Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS
Investigating the proposed method for cross-dataset intra-domain transfer learning, we find that 1) our method performs with an average weighted F1 score of 0. 80 while requiring 3-times fewer epochs for model training compared to the non-transfer approach, 2) can achieve an F1 score of 0. 75 with only 230 data samples, and 3) our transfer approach outperforms the state-of-the-art in precision drop by up to 12 percentage points for unseen appliances.
Towards Sustainable Deep Learning for Wireless Fingerprinting Localization
A detailed performance evaluation shows that the proposed model producesonly 58 % of the carbon footprint while maintaining 98. 7 % of the overall performance compared to state of the art model external to our group.
Resource-aware Time Series Imaging Classification for Wireless Link Layer Anomalies
The number of end devices that use the last mile wireless connectivity is dramatically increasing with the rise of smart infrastructures and require reliable functioning to support smooth and efficient business processes.
Learning to Detect Anomalous Wireless Links in IoT Networks
After decades of research, the Internet of Things (IoT) is finally permeating real-life and helps improve the efficiency of infrastructures and processes as well as our health.
