In:
Applied Mechanics and Materials, Trans Tech Publications, Ltd., Vol. 882 ( 2018-7), p. 96-108
Abstract:
This paper addresses the problem of efficiently operating a flexible manufacturing machine in an electricity micro-grid featuring a high volatility of electricity prices. The problem of finding the optimal control policy is formulated as a sequential decision making problem under uncertainty where, at every time step the uncertainty comes from the lack of knowledge about fu-ture electricity consumption and future weather dependent energy prices. We propose to address this problem using deep reinforcement learning. To this purpose, we designed a deep learning architecture to forecast the load profile of future manufacturing schedule from past production time series. Combined with the forecast of future energy prices, the reinforcement-learning algorithm is trained to perform an online optimization of the production ma-chine in order to reduce the long-term energy costs. The concept is empirical-ly validated on a flexible production machine, where the machine speed can be optimized during the production.
Type of Medium:
Online Resource
ISSN:
1662-7482
DOI:
10.4028/www.scientific.net/AMM.882
DOI:
10.4028/www.scientific.net/AMM.882.96
Language:
Unknown
Publisher:
Trans Tech Publications, Ltd.
Publication Date:
2018
detail.hit.zdb_id:
2251882-4
Permalink