Leonard Burg
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Leonard Burg has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Antonello Monti
Fraunhofer Institute for Applied Information Technology
1 shared publication - Gonca Gürses-Tran
RWTH Aachen University, Aachen, Germany
1 shared publication - Reinhard Madlener
Institute for Future Energy Consumer Needs and Behavior (FCN), School of Business and Economics/E.ON Energy Research Center, RWTH Aachen University, Mathieustrasse 10, 52074 Aachen, Germany
1 shared publication
Latest Publications
- Open Access
Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels
Power system operators are confronted with a multitude of new forecasting tasks to ensure a constant supply security despite the decreasing number of fully controllable energy producers. With this paper, we aim to facilitate the selection of suitable forecasting approaches for the load forecasting problem. First, we provide a classification of load forecasting cases in two dimensions: temporal and hierarchical. Then, we identify typical features and models for forecasting and compare their applicability in a structured manner depending on six previously defined cases. These models are compared against real data in terms of their computational effort and accuracy during development and testing. From this comparative analysis, we derive a generic guide for the selection of the best prediction models and features per case.
Open AccessComparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels
Power system operators are confronted with a multitude of new forecasting tasks to ensure a constant supply security despite the decreasing number of fully controllable energy producers. With this paper, we aim to facilitate the selection of suitable forecasting approaches for the load forecasting problem. First, we provide a classification of load forecasting cases in two dimensions: temporal and hierarchical. Then, we identify typical features and models for forecasting and compare their applicability in a structured manner depending on six previously defined cases. These models are compared against real data in terms of their computational effort and accuracy during development and testing. From this comparative analysis, we derive a generic guide for the selection of the best prediction models and features per case.