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基于强化学习的建筑能源管理系统架构用于能源使用优化

Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization.

作者信息

Park Sanguk, Park Sangmin, Choi Myeong-In, Lee Sanghoon, Lee Tacklim, Kim Seunghwan, Cho Keonhee, Park Sehyun

机构信息

School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Korea.

出版信息

Sensors (Basel). 2020 Aug 31;20(17):4918. doi: 10.3390/s20174918.

DOI:10.3390/s20174918
PMID:32878089
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7506749/
Abstract

Currently, many intelligent building energy management systems (BEMSs) are emerging for saving energy in new and existing buildings and realizing a sustainable society worldwide. However, installing an intelligent BEMS in existing buildings does not realize an innovative and advanced society because it only involves simple equipment replacement (i.e., replacement of old equipment or LED (Light Emitting Diode) lamps) and energy savings based on a stand-alone system. Therefore, artificial intelligence (AI) is applied to a BEMS to implement intelligent energy optimization based on the latest ICT (Information and Communications Technologies) technology. AI can analyze energy usage data, predict future energy requirements, and establish an appropriate energy saving policy. In this paper, we present a dynamic heating, ventilation, and air conditioning (HVAC) scheduling method that collects, analyzes, and infers energy usage data to intelligently save energy in buildings based on reinforcement learning (RL). In this regard, a hotel is used as the testbed in this study. The proposed method collects, analyzes, and infers IoT data from a building to provide an energy saving policy to realize a futuristic HVAC (heating system) system based on RL. Through this process, a purpose-oriented energy saving methodology to achieve energy saving goals is proposed.

摘要

目前，许多智能建筑能源管理系统（BEMS）正在涌现，以实现新建和现有建筑的节能，并在全球范围内实现可持续发展的社会。然而，在现有建筑中安装智能BEMS并不能实现创新和先进的社会，因为它只涉及简单的设备更换（即更换旧设备或LED（发光二极管）灯）以及基于独立系统的节能。因此，人工智能（AI）被应用于BEMS，以基于最新的ICT（信息通信技术）技术实现智能能源优化。AI可以分析能源使用数据，预测未来能源需求，并制定适当的节能政策。在本文中，我们提出了一种动态供热、通风和空调（HVAC）调度方法，该方法基于强化学习（RL）收集、分析和推断能源使用数据，以智能地实现建筑节能。在这方面，本研究以一家酒店作为测试平台。所提出的方法从建筑物收集、分析和推断物联网数据，以提供节能政策，从而基于RL实现未来的HVAC（供热系统）系统。通过这个过程，提出了一种面向目标的节能方法来实现节能目标。

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本文引用的文献

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Sensors (Basel). 2021 Mar 19;21(6):2152. doi: 10.3390/s21062152.