Feasibility study on the use of artificial intelligence for a medium-term (24-72h) small hydropower forecast in the Salzburg model region
Abstract
One of the important tasks of Austrian Power Grid AG (APG) is the medium-term forecast of energy production from hydropower plants in order to be able to coordinate and plan the availability and utilization of the Austrian electricity grid accordingly. The objective of the planned project is to improve the medium-term forecast (24, 48 and 72 hours) for the "medium" small hydropower plants. An initial focus will be placed on the Salzburg model region, where the data situation for the development and implementation of AI forecasting methods is very good. This initial feasibility study will be divided into several phases: In consultation with the client, initially two AI methods (XGBoost, and Long-Short-Term-Memory (LSTM) models) will be tested and optimized with the VTW's as the sole input variables for the locations of the CHP plants. In further steps, static catchment area characteristics (topography, soil, geology, climate, vegetation) are integrated into the methods as additional information. External drivers such as precipitation and weather forecasts from ZAMG/GeoSphere or model-based estimates of the so-called snow-water equivalent of the existing snow cover are then taken into account.
keywords Machine Learning Water Energy
Publikationen
Project staff
Karsten Schulz
Univ.Prof. Dipl.Geoökol. Dr.rer.nat. Karsten Schulz
karsten.schulz@boku.ac.at
Tel: +43 1 47654-81699
Project Leader
15.11.2023 - 14.03.2025
Mathew Herrnegger
Dipl.-Ing. Dr.nat.techn. Mathew Herrnegger
mathew.herrnegger@boku.ac.at
Tel: +43 1 47654-81618
Sub Projectleader
15.11.2023 - 14.03.2025
Moritz Feigl
Dipl.-Ing. Dr. Moritz Feigl
moritz.feigl@boku.ac.at
Tel: +43 1 47654-81610
Project Staff
15.11.2023 - 14.03.2025
Christoph Klingler
Dipl.-Ing. Christoph Klingler
christoph.klingler@boku.ac.at
Tel: +43 1 47654-81610
Project Staff
15.11.2023 - 14.03.2025