Hans Ivar Skjelbred
Seniorforsker
Hans Ivar Skjelbred
Seniorforsker
Publikasjoner og ansvarsområder
Machine Learning for Hydropower Scheduling: State of the Art and Future Research Directions
This paper investigates and discusses the current and future role of machine learning (ML) within the hydropower sector. An overview of the main applications of ML in the field of hydropower operations is presented to show the most common topics that have been addressed in the scientific literature...
The start-of-the-art application of machine learning
Using machine learning to reduce mixed-integer variables in hydro scheduling
Using machine learning for time-dependent commands
Command status given the feature list
Summary of the feature list
Dynamic incorporation of nonlinearity into MILP formulation for short-term hydro scheduling
Optimization tools are widely used for solving the short-term hydro scheduling (STHS) problem in a cascaded hydro system. In a mixed integer linear programming (MILP)-based formulation, the nonlinear and non-convex hydropower production function (HPF) is represented by piecewise linear approximation...
Strategic Research Agenda of the EERA Joint Programme Hydropower
An overview on formulations and optimization methods for the unit-based short-term hydro scheduling problem
The short-term hydro scheduling (STHS) problem aims at determining the optimal power generation schedules for either a single hydropower plant or an integrated system of cascaded watercourses during a time horizon from a single day to one week. Traditionally, an aggregated plant concept is usually...