.png){kind=icon}
返回
Autonomous pre-conditioning and improved personalization in shared workspaces through data-driven predictive control
.png){kind=icon}
OA
AI
DOI：10.1016/j.enbuild.2023.112897.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper studies the problem of indoor zone temperature control in shared workspaces equipped with heterogeneous heating and cooling sources with the goal of increased energy savings and environment personalization. Shared workspaces typically witness distinct, pre-scheduled intervals when they are occupied or are unoccupied. In this work, we develop indoor climate control strategies for each of these intervals. For the interval when the workspace is unoccupied, we propose multiple time-bound control strategies for pre-conditioning the workspace in preparation for a scheduled activity (Phase I). For the interval when the workspace is occupied, we propose a separate control strategy which enhances the thermal comfort of the occupants by harnessing the spatial differentiation of the thermal environment to satisfy the different temperature preferences of the individuals (Phase II). Utilizing a physical test-bed and data-driven model learning, we show that our proposed pre-conditioning strategies in Phase I are less computationally expensive than conventional model predictive control (MPC). For Phase II, we use a low complexity quadratic program to minimize the thermal discomfort experienced by individuals based on their temperature preferences. The experimental results show that for Phase I, the proposed control policies can save a significant amount of energy and achieve the desired mean temperature in the space fairly accurately. We further note that for Phase II, the control scheme can achieve a significant spatial differentiation in temperature towards satisfying the occupants' thermal preferences.(c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Energy efficiency
Indoor climate control
HVAC
Environment personalization
MPC
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.1
论文数:
1.6W
被引数:
6.8W
机构
引用论文
A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings优化办公楼地板辐射供暖和制冷系统性能的模型预测控制策略.png)
APPLIED ENERGYOAAI
APPLIED ENERGYIF11
OAAI分享收藏Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings面向商业建筑暖通空调控制的多智能体深度强化学习OAAI
OAAI分享收藏Grey-box modeling and application for building energy simulations-A critical review建筑能耗模拟的灰箱建模与应用 -- 综述OAAI
OAAI分享收藏Simplified data-driven models for model predictive control of residential buildings住宅建筑模型预测控制的简化数据驱动模型ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏分享收藏Experimental analysis of simulated reinforcement learning control for active and passive building thermal storage inventory Part 2: Results and analysisENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏
分享收藏
查看更多内容