4.6 Article

Event-Based HVAC Control-A Complexity-Based Approach

期刊

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2018.2844258

关键词

Discrete event dynamic systems; Markov decision processes (MDPs); event-based optimization; building energy saving; complexity

资金

  1. National Key Research and Development Program of China [2016YFB0901900]
  2. National Natural Science Foundation of China [61673229, 61222302, 61174072, 91224008, 61221063, U1301254]
  3. 111 International Collaboration Project of China [B06002]
  4. Program for New Star of Science and Technology in Beijing [xx2014B056]

向作者/读者索取更多资源

The optimal control of the heating, ventilation, and air-conditioning (HVAC) system in buildings has a significant energy saving potential and therefore is of a great practical interest. An event-based HVAC control adjusts control actions when certain events occur, which may be faster and more scalable than state-based or time-driven control methods. However, events may capture either local or global changes in the rooms. The choice of events is a tradeoff between the computational efficiency and the control performance. This challenging problem remains open. We consider this as an important problem in this paper and make three major contributions. First, we define local and global events for the HVAC control problem. The complexity of these event-based control policies is defined. Second, based on hypothesis testing, we develop a method to select events that capture a sufficient state information and with a relatively small event space. Third, we demonstrate the performance of this method on two groups of examples, including one group of small-scale problems for the proof of concept and the other group of large-scale problems in the HVAC control. It is shown that our method outperforms the Levin search, which is a traditional complexity-based search method and finds event-based HVAC control policies with a good performance.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Computer Science, Artificial Intelligence

An Adaptive Deep Belief Network With Sparse Restricted Boltzmann Machines

Gongming Wang, Junfei Qiao, Jing Bi, Qing-Shan Jia, MengChu Zhou

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Computer Science, Artificial Intelligence

Soft-sensing of Wastewater Treatment Process via Deep Belief Network with Event-triggered Learning

Gongming Wang, Qing-Shan Jia, MengChu Zhou, Jing Bi, Junfei Qiao

Summary: This paper proposes a Deep Belief Network with Event-triggered Learning (DBN-EL) to improve the efficiency and accuracy of soft-sensing model in WWTP. Through defining events, designing event-triggered learning strategy, and conducting convergence analysis, the effectiveness of this method in practical WWTP applications is demonstrated.

NEUROCOMPUTING (2021)

Article Automation & Control Systems

A Computing Budget Allocation Method for Minimizing EV Charging Cost Using Uncertain Wind Power

Zhaoyu Jiang, Qing-Shan Jia, Xiaohong Guan

Summary: The idea of utilizing wind power to charge electric vehicles has gained attention for its potential in reducing air pollution. However, challenges arise due to uncertainties in wind power generation and EV charging demands. This study focuses on addressing the issues through simulation-based policy improvement, with a specific focus on computing budget allocation for decision-making in online applications. Research findings demonstrate the importance of addressing uncertainty in wind power forecasting for EV charging decisions while comparing different allocation methods through numerical experiments.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2021)

Review Computer Science, Artificial Intelligence

Artificial neural networks for water quality soft-sensing in wastewater treatment: a review

Gongming Wang, Qing-Shan Jia, MengChu Zhou, Jing Bi, Junfei Qiao, Abdullah Abusorrah

Summary: This paper presents a comprehensive survey on water quality soft-sensing in wastewater treatment processes using artificial neural networks (ANNs). It covers problem formulation, common models, practical examples, and performance discussions. Various soft-sensing models are compared in terms of accuracy, efficiency, and complexity, with factors affecting the accuracy discussed as well. Challenges in soft-sensing models of WWTP are also pointed out for future exploration.

ARTIFICIAL INTELLIGENCE REVIEW (2022)

Article Computer Science, Information Systems

On large action space in EV charging scheduling optimization

Zhaoyu Jiang, Qing-Shan Jia, Xiaohong Guan

Summary: This paper investigates the charging scheduling problem for electric vehicles (EVs), considering the uncertainty in renewable power generation and the randomness in EV charging demand. By exploring the structural property of the problem, an urgency index is developed to rank the EVs, and three methods are applied to search in the action space. Numerical demonstrations show that simulation-based policy improvement (SBPI) improves the performance of base policies in various cases compared to the CPLEX-based method.

SCIENCE CHINA-INFORMATION SCIENCES (2022)

Article Multidisciplinary Sciences

DNA dynamics and computation based on toehold-free strand displacement

Hong Kang, Tong Lin, Xiaojin Xu, Qing-Shan Jia, Richard Lakerveld, Bryan Wei

Summary: The study introduces a dynamic switch scheme for DNA nanostructures, utilizing toehold-free strand displacement. Through simulations and experiments, the unique properties of toehold-free strand displacement in equilibrium control are demonstrated, along with showcasing the potential applications of controllable dynamics.

NATURE COMMUNICATIONS (2021)

Article Computer Science, Artificial Intelligence

Deep Learning-Based Model Predictive Control for Continuous Stirred-Tank Reactor System

Gongming Wang, Qing-Shan Jia, Junfei Qiao, Jing Bi, MengChu Zhou

Summary: This work introduces a deep learning-based model predictive control (DeepMPC) for modeling and controlling a continuous stirred-tank reactor (CSTR) system. DeepMPC achieves high performance in system identification and control through automatic determination of size and quadratic optimization.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

Article Engineering, Electrical & Electronic

Efficient Real-Time EV Charging Scheduling via Ordinal Optimization

Teng Long, Qing-Shan Jia, Gongming Wang, Yu Yang

Summary: This paper presents an efficient and scalable real-time scheduling method for handling the charging demands of plug-in electric vehicles (PEV), demonstrating through simulations that the proposed method provides high computation efficiency and scalability while reducing operating costs for charging stations. Compared to existing methods, it outperforms in terms of charging policy search capabilities and performance guarantee.

IEEE TRANSACTIONS ON SMART GRID (2021)

Article Automation & Control Systems

Joint Scheduling of Deferrable Demand and Storage With Random Supply and Processing Rate Limits

Jiangliang Jin, Liangliang Hao, Yunjian Xu, Junjie Wu, Qing-Shan Jia

Summary: The study focused on the joint scheduling of deferrable demands and storage systems, proposing an optimal index-based priority rule and recommending the use of reinforcement learning methods for energy procurement decisions, achieving a significant improvement in system cost reduction compared to existing RL methods.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2021)

Article Automation & Control Systems

A Structural Property of Charging Scheduling Policy for Shared Electric Vehicles With Wind Power Generation

Qing-Shan Jia, Junjie Wu

Summary: This work focuses on optimizing the charging scheduling policy for shared electric vehicles integrated with wind power generation. The research extends the least-laxity-longer-processing-time-first principle and demonstrates improved performance over existing algorithms. The new algorithm shows near-optimal results and significant speed improvement compared to CPLEX.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2021)

Article Automation & Control Systems

Multiagent Dynamic Task Assignment Based on Forest Fire Point Model

Jie Chen, Yuqian Guo, Zhifeng Qiu, Bin Xin, Qing-Shan Jia, Weihua Gui

Summary: This article investigates multiagent dynamic task assignment for forest fires based on a point model, providing optimal solutions for static task assignment and proposing a dynamic task assignment scheme based on global information. The simulation on MATLAB platform verifies the performance of the proposed scheme when compared with a multistage global auction algorithm.

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING (2022)

Article Automation & Control Systems

Joint Optimization for Coordinated Charging Control of Commercial Electric Vehicles Under Distributed Hydrogen Energy Supply

Teng Long, Qing-Shan Jia

Summary: The article introduces a novel architecture consisting of hydrogen production stations, fast-charging stations, and commercial electric vehicles to optimize hydrogen energy dispatch and EV charging location selection. Case studies confirm the effectiveness of the architecture in reducing operating costs and improving performance by at least 13%.

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY (2022)

Proceedings Paper Automation & Control Systems

Decentralized Multi-agent Reinforcement Learning with Multi-time Scale of Decision Epochs

Junjie Wu, Kuo Li, Qing-Shan Jia

2020 59TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC) (2020)

Proceedings Paper Automation & Control Systems

A Game-Theoretic Reinforcement Learning Approach for Adaptive Interaction at Intersections

Xinze Jin, Kuo Li, Qing-Shan Jia, Huaxia Xia, Yu Bai, Dongchun Ren

2020 CHINESE AUTOMATION CONGRESS (CAC 2020) (2020)

Proceedings Paper Automation & Control Systems

Predictive Maintenance of VRLA Batteries in UPS towards Reliable Data Centers

Jing-Xian Tang, Jin-Hong Du, Yiting Lin, Qing-Shan Jia

IFAC PAPERSONLINE (2020)

暂无数据