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research-article

Automated Design of Energy Efficient Control Strategies for Building Clusters Using Reinforcement Learning

[+] Author and Article Information
Philip Odonkor

Graduate Research Assistant, Department of Mechanical and Aerospace Engineering, University at Buffalo-SUNY, Buffalo, NY 14260
podonkor@buffalo.edu

Kemper Lewis

Professor, Department of Mechanical and Aerospace Engineering, University at Buffalo-SUNY, Buffalo, NY 14260
kelewis@buffalo.edu

1Corresponding author.

ASME doi:10.1115/1.4041629 History: Received July 06, 2018; Revised September 21, 2018

Abstract

The control of shared energy assets within building clusters has traditionally been confined to a discrete action space, owing in part to a computationally intractable decision space. In this work, we leverage the current state of the art in reinforcement learning for continuous control tasks, the Deep Deterministic Policy Gradient (DDPG) algorithm, towards addressing this limitation. The goals of this paper are twofold; (i) to design an efficient charged/discharged dispatch policy for a shared battery system within a building cluster, (ii) to address the continuous domain task of determining how much energy should be charged/discharged at each decision cycle. Experimentally, our results demonstrate an ability to exploit factors such as energy arbitrage, along with the continuous action space towards demand peak minimization. This approach is shown to be computationally tractable, achieving efficient results after only 5 hours of simulation. Additionally, the agent showed an ability to adapt to different building clusters, designing unique control strategies to address the energy demands of the unique clusters studied.

Copyright (c) 2018 by ASME
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