Jakob Kasbauer, M.Sc.

Mobile and Embedded Systems (Universität Passau) Forschungsgruppe: Applied Artificial Intelligence Datenanalyse und Machine Learning

Wissenschaftlicher Mitarbeiter

Grafenau

08552/975620-34


Beitrag (Sammelband oder Tagungsband)
  • Sebastian Wilhelm
  • Dietmar Jakob
  • Jakob Kasbauer
  • Diane Ahrens
GeLaP: German Labeled Dataset for Power Consumption. [Accepted for publication]
  • 2021
Due to the increasing spread of smart meters numerous researchers are currently working on disaggregating the power consumption data. This procedure is commonly known as Non-Intrusive Load Monitoring (NILM). However most approaches to energy disaggregation first require a labeled dataset to train these algorithms.In this paper we present a new labeled power consumption dataset that was collected in 20 private households in Germany between September 2019 and July 2020. For this purpose the total power consumption of each household was measured with a commercial available smart meter and the individual consumption data of 10 selected household appliances were collected.
  • TC Grafenau
  • DIGITAL
Beitrag (Sammelband oder Tagungsband)
  • Sebastian Wilhelm
  • Dietmar Jakob
  • Jakob Kasbauer
  • M. Dietmeier
Organizational, Technical, Ethical and Legal Requirements of Capturing Household Electricity Data for Use as an AAL System
  • 2020

DOI: 10.1007/978-981-15-5856-6_38

Due to demographic change elderly care is one of the major challenges for society in near future fostering new services to support and enhance the life quality of the elderly generation. A particular aspect is the desire to live in one’s homes instead of hospitals and retirement homes as long as possible. Therefore it is essential to monitor the health status i.e. the activity of the individual. In our data-driven society data is collected at an increasing rate enabling personalized services for our daily life using machine-learning and data mining technologies. However the lack of labeled datasets from a realistic environment hampers research for training and evaluating algorithms. In the project BLADL we use data mining technologies to gauge the health status of elderly people. Within this work we discuss the challenges and caveats both from a technical and ethical perspectives to create such a dataset.
  • TC Grafenau
  • Angewandte Wirtschaftswissenschaften
  • DIGITAL