A Fast Technique for Smart Home Management: ADP With Temporal Difference Learning

This paper presents a computationally efficient smart home energy management system (SHEMS) using an approximate dynamic programming (ADP) approach with temporal difference learning for scheduling distributed energy resources. This approach improves the performance of an SHEMS by incorporating stoch...

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Veröffentlicht in:	IEEE transactions on smart grid Jg. 9; H. 4; S. 3291 - 3303
Hauptverfasser:	Keerthisinghe, Chanaka, Verbic, Gregor, Chapman, Archie C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.07.2018
Schlagworte:	approximate dynamic programming Australia Batteries Demand response distributed energy resources Dynamic programming Energy management Optimization smart home energy management Smart homes stochastic mixed-integer linear programming Stochastic processes temporal difference learning value function approximation
ISSN:	1949-3053, 1949-3061
Online-Zugang:	Volltext
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