Výsledky vyhledávání - "Gilles Van Kriekinge"

Akademický článek

Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems

Autor: Julian Ruddick, Glenn Ceusters, Gilles Van Kriekinge, Evgenii Genov, Cedric De Cauwer, Thierry Coosemans, Maarten Messagie

Publikováno v: Energy and AI, Vol 18, Iss , Pp 100448- (2024)

Recent advancements in machine learning based energy management approaches, specifically reinforcement learning with a safety layer (OptLayerPolicy) and a metaheuristic algorithm generating a decision tree control policy (TreeC), have shown promise.

Externí odkaz: https://doaj.org/article/bd444a2ad35c4ed4aab92d1d09c36f24

Zobrazit plný text záznamu

Akademický článek

Peak shaving and cost minimization using model predictive control for uni- and bi-directional charging of electric vehicles

Autor: Gilles Van Kriekinge, Cedric De Cauwer, Nikolaos Sapountzoglou, Thierry Coosemans, Maarten Messagie

Publikováno v: Energy Reports, Vol 7, Iss , Pp 8760-8771 (2021)

Uni- and bi-directional electric vehicle charging schedulers are key enablers in the mitigation of the negative impacts, such as peak powers, of electric vehicles and renewable energy sources on the grid. This paper presents two electric vehicle char

Externí odkaz: https://doaj.org/article/9589a4824230447381f83a5a80500ac2

Zobrazit plný text záznamu

Akademický článek

Electric Vehicle Charging Sessions Generator Based on Clustered Driver Behaviors

Autor: Gilles Van Kriekinge, Cedric De Cauwer, Nikolaos Sapountzoglou, Thierry Coosemans, Maarten Messagie

Publikováno v: World Electric Vehicle Journal, Vol 14, Iss 2, p 37 (2023)

Increasing penetration of electric vehicles brings a set of challenges for the electricity system related to its energy, power and balance adequacy. Research related to this topic often requires estimates of charging demand in various forms to feed v

Externí odkaz: https://doaj.org/article/5444131fc88a4bac99d57e6c780d787c

Zobrazit plný text záznamu

Akademický článek

Day-Ahead Forecast of Electric Vehicle Charging Demand with Deep Neural Networks

Autor: Gilles Van Kriekinge, Cedric De Cauwer, Nikolaos Sapountzoglou, Thierry Coosemans, Maarten Messagie

Publikováno v: World Electric Vehicle Journal, Vol 12, Iss 4, p 178 (2021)

The increasing penetration rate of electric vehicles, associated with a growing charging demand, could induce a negative impact on the electric grid, such as higher peak power demand. To support the electric grid, and to anticipate those peaks, a gro

Externí odkaz: https://doaj.org/article/ad51a7080ed24ff2bcac83964c5894b8

Zobrazit plný text záznamu