Jakob Kasbauer, M.Sc.
Mobile and Embedded Systems (University of Passau) Research Group: Applied Artificial Intelligence Data Analysis and Machine Learning
Academic Staff
Beitrag (Sammelband oder Tagungsband)
- Sebastian Wilhelm
- Dietmar Jakob
- Jakob Kasbauer
- Diane Ahrens
In: Proceedings of Sixth International Congress on Information and Communication Technology. null (Lecture Notes in Networks and Systems)
- 2022
DOI: 10.1007/978-981-16-2377-6_5
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
- Diane Ahrens
- 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
Zeitschriftenartikel
- Sebastian Wilhelm
- Jakob Kasbauer
In: Sensors
- 2021
DOI: 10.3390/s21238036
Numerous approaches exist for disaggregating power consumption data, referred to as non-intrusive load monitoring (NILM). Whereas NILM is primarily used for energy monitoring, we intend to disaggregate a household’s power consumption to detect human activity in the residence. Therefore, this paper presents a novel approach for NILM, which uses pattern recognition on the raw power waveform of the smart meter measurements to recognize individual household appliance actions. The presented NILM approach is capable of (near) real-time appliance action detection in a streaming setting, using edge computing. It is unique in our approach that we quantify the disaggregating uncertainty using continuous pattern correlation instead of binary device activity states. Further, we outline using the disaggregated appliance activity data for human activity recognition (HAR). To evaluate our approach, we use a dataset collected from actual households. We show that the developed NILM approach works, and the disaggregation quality depends on the pattern selection and the appliance type. In summary, we demonstrate that it is possible to detect human activity within the residence using a motif-detection-based NILM approach applied to smart meter measurements.
- TC Grafenau
- DIGITAL
Beitrag (Sammelband oder Tagungsband)
- Sebastian Wilhelm
- Dietmar Jakob
- Jakob Kasbauer
- M. Dietmeier
- 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