Analysis of ensemble models in the medium term hydropower scheduling

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The medium term hydropower scheduling (MTHS) problem involves an attempt to determine, for each time stage of the planning period, the amount of generation at each hydro plant which will maximize the expected future benefits throughout the planning period, while respecting plant operational constraints. Besides, it is important to emphasize that this decision-making has been done based mainly on inflow earliness knowledge. To perform the forecast of a determinate basin, it is possible to use some intelligent computational approaches. In this paper one considers the Dynamic Programming (DP) with the inflows given by their average values, thus turning the problem into a deterministic one which the solution can be obtained by deterministic DP (DDP). The performance of the DDP technique in the MTHS problem was assessed by simulation using the ensemble prediction models. Features and sensitivities of these models are discussed. © 2012 IEEE.



Artificial Intelligence, Dynamic Programming, Ensembles, Inflow Forecast, Medium Term Hydropower Scheduling, Predictive Models, Average values, Computational approach, Ensemble models, Ensemble prediction, Future benefits, Hydro plants, Hydropower scheduling, Inflow forecast, Medium term, Operational constraints, Planning period, Predictive models, Artificial intelligence, Dynamic programming

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IEEE Power and Energy Society General Meeting.