Machine Learning Applied to the Operation of Fully Renewable Energy Systems

This work presents a novel learning algorithm for the operation policy of power systems trying to minimize the cost of fulfilling the energy demand. The algorithm improves upon the classical reinforcement learning methods by controlling the sampling variance in the estimation of the future cost spat...

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Vydané v:	2023 IEEE PES GTD International Conference and Exposition (GTD) s. 139 - 143
Hlavní autori:	Chaer, Ruben, Ramirez, Ignacio, Casaravilla, Gonzalo
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.05.2023
Predmet:	Approximate Stochastic Dynamic Programming Costs Heuristic algorithms Lakes Power system dynamics Reinforcement learning Reinforcement Machine Learning Renewable Energies Solar energy Stochastic processes
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