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Kategoriebasierte Lastprognose in Niederspannungsnetzen zur Day-Ahead-Koordinierung haushaltsnaher Flexibilität
Citation Link: https://doi.org/10.15480/882.16569
Publikationstyp
Master Thesis
Date Issued
2025
Sprache
German
Author(s)
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2025-11-24
Institute
TORE-DOI
Citation
Technische Universität Hamburg (2025)
The increasing share of decentralized producers and flexible consumers complicates the maintenance of grid stability and raises the requirements for the control and monitoring of distribution networks. As part of a research project, a coordination function (COF) is being developed that performs day-ahead scheduling of flexible household loads to proactively prevent grid congestion. Since the schedule data of many households are incomplete, uncertainties arise in grid assessment and flexibility planning. This work addresses this issue and extends the existing concept by developing a forecasting method that provides the COF with a complete and reliable data basis. To this end, a data-driven approach is developed that applies the HDBSCAN clustering algorithm to categorize households with similar technical characteristics and consumption behavior, thereby forming representative load profiles. Missing schedules can thus be plausibly reconstructed. Subsequently, a gradient boosting regression refines the cluster-based forecasts by incorporating additional influencing factors such as weather, price, and grid data. The results demonstrate that the combination of clustering and regression analysis provides an effective approach for forecasting household schedules. This highly improves the data foundation for the COF and enables robust load forecasting even under limited data availability. The cluster forecast provides a high level of predictive accuracy at the aggregated level, while the regression analysis further improves accuracy at the household level. Individual households with highly specific consumption patterns can only be represented to a limited extent, which may affect the efficient use of flexibility in the grid but has only a minor impact on the overall reliability of the forecast. Future work should integrate real smart meter data to validate and improve the model quality and examine the use of deep learning-based methods to capture complex temporal patterns and short-term consumption changes even more accurately.
Subjects
Coordination function
forecasting household schedules
HDBSCAN clustering
gradient boosting regression
load forecast
grid flexibility
DDC Class
621.3: Electrical Engineering, Electronic Engineering
006.3: Artificial Intelligence
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Masterarbeit_Joost_54119.pdf
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