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Document type:
Report / Forschungsbericht 
Author(s):
Johannes Feldmaier, Dominik Meyer, Hao Shen, Klaus Diepold 
Title:
Monitoring and Prediction in Smart Energy Systems via Multi-timescale Nexting 
Volume:
cs:SY 
Abstract:
Reliable prediction of system status is a highly demanded functionality of smart energy systems, which can enable users or human operators to react quickly to potential future system changes. By adopting the multi-timescale nexting method, we develop an architecture of human-in-the-loop energy control system, which is capable of casting short-term predictive information about the specific smart energy system. The developed architecture does either require a system model nor additional acquisitio...    »
 
Keywords:
reinforcement learning, temporal difference learning, nexting, smart energy systems 
Contracting organization:
Lehrstuhl für Datenverarbeitung 
Publisher:
eprint arXiv:1607.05015 
Date of publication:
19.07.2016 
Year:
2016 
Format:
Text