Batch Reinforcement Learning for Smart Home Energy Management
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Computational Sustainability. Pages 2561-2566.
Abstract
Smart grids enhance power grids by integrating electronic equipment, communication systems and computational tools. In a smart grid, consumers can insert energy into the power grid. We propose a new energy management system (called RLbEMS) that autonomously defines a policy for selling or storing energy surplus in smart homes. This policy is achieved through Batch Reinforcement Learning with historical data about energy prices, energy generation, consumer demand and characteristics of storage systems. In practical problems, RLbEMS has learned good energy selling policies quickly and effectively. We obtained maximum gains of 20.78% and 10.64%, when compared to a Naive-greedy policy, for smart homes located in Brazil and in the USA, respectively. Another important result achieved by RLbEMS was the reduction of about 30% of peak demand, a central desideratum for smart grids.
@inproceedings{ijcai2015p363,
title = {Batch Reinforcement Learning for Smart Home Energy Management},
author = {Berlink, Heider and Costa, Anna Helena Reali},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {2561--2566},
year = {2015},
month = {7},
note = {Special Track on Computational Sustainability},
url = {https://ijcai.org/proceedings/2015/363},
}
TY - CPAPER TI - Batch Reinforcement Learning for Smart Home Energy Management AU - Berlink, Heider AU - Costa, Anna Helena Reali T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 2561 EP - 2566 UR - https://ijcai.org/proceedings/2015/363 ER -Copied
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