Adaptive multi-agent control of hvac systems for residential demand response using batch reinforcement learning

Kaempf, Jérôme (School of Engineering and Architecture (HEIA-FR), HES-SO // University of Applied Sciences Western Switzerland)

Demand response allows consumers to reduce their electrical consumption during periods of peak energy use. This reduces the peaks of electrical demand, and, consequently, the wholesale electricity prices. However, buildings must coordinate with each other to avoid delaying their electricity consumption simultaneously, which would create new, delayed peaks of electrical demand. In this work, we examine this coordination using batch reinforcement learning (BRL). BRL does not require a model, and allows the buildings to adapt over time to the optimal behavior. We implemented our controller in CitySim, a building simulator, using TensorFlow, a machine learning library.


Conference Type:
full paper
Faculty:
Ingénierie et Architecture
School:
HEIA-FR
Institute:
Energy - Institut de recheche appliquée en systèmes énergétiques
Subject(s):
Ingénierie
Publisher:
Chicago, IL, USA, 26-28 September 2018
Date:
2018-09
Chicago, IL, USA
26-28 September 2018
Pagination:
8 p.
Published in:
Proceedings of 2018 Building Performance Analysis Conference and SimBuild co-organized by ASHRAE and IBPSA-USA, 26-28 September 2018, Chicago, IL, USA
Appears in Collection:



 Record created 2018-12-04, last modified 2018-12-11

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