Open AccessOnline learning based on a novel cost function for system power management
Author(s) -
Wang Xiang,
Li Lin,
Wang Weike,
Du Pei
Publication year - 2018
Publication title -
iet computers and digital techniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.219
H-Index - 46
ISSN - 1751-861X
DOI - 10.1049/iet-cdt.2017.0211
Subject(s) - computer science , function (biology) , power (physics) , power management system , electric power system , power management , state (computer science) , reliability engineering , mathematical optimization , engineering , algorithm , mathematics , physics , quantum mechanics , evolutionary biology , biology
A novel system power management technique is proposed that employs a novel cost function based on state‐action. Compared with the conventional algorithm, by using multiple parameter constraints in cost function of power management framework, the improved Q‐learning can effectively make decisions to achieve a rational optimisation room. The proposed power management framework does not need any prior data and is running on a power model. As uncertainties can be effectively captured and modelled, the framework based on the model can help to explore an ideal trade‐off and converge to the best power management policy. The results obtained showed that improved algorithm achieved remarkable significance.