Donghun Kim

Bio: Donghun Kim is an academic researcher from Purdue University. The author has contributed to research in topics: Model predictive control & Optimal control. The author has an hindex of 13, co-authored 37 publications receiving 477 citations. Previous affiliations of Donghun Kim include Lawrence Berkeley National Laboratory.

Reduced-order building modeling for application to model-based predictive control

Abstract: This paper presents methods and results for representing the complex thermal network of a building envelope and interior in the form of reduced-order state-space equations that can more easily be applied in model-based predictive and other advanced control approaches. The complexities of heat transfer phenomena through glazings and long wavelength exchanges among walls make the representation difficult. The model employs the net radiosity method for long-wave interaction, one-dimensional transient conduction through walls, conductive and convective coupling between zones, etc. Model order reduction is applied to simplify the state-space representation and case study results are presented.

A general approach for generating reduced-order models for large multi-zone buildings

Abstract: Many approaches for reducing model complexities have been suggested for characterizing the dynamic behaviour of buildings. However, previous work has focused on buildings that have a relatively small number of zones and there have been very limited comparisons with experimental data or with the predictions and computational requirements of validated simulation models that are widely used. In particular, the applicability to large multi-zone buildings has not been addressed. This is an important application, where the cost to generate a reduced-order model (ROM) can be significant, but the benefits of ROM for analysis and design for building system can be tremendous. This paper presents a general and computationally efficient approach for generating a ROM from a detailed representation of the dynamics of commercial sized multi-zone buildings and provides comparisons of predictions and computational requirements with a commercial building simulation tool.

Multi-Agent Systems: A Survey

Abstract: Multi-agent systems (MASs) have received tremendous attention from scholars in different disciplines, including computer science and civil engineering, as a means to solve complex problems by subdividing them into smaller tasks. The individual tasks are allocated to autonomous entities, known as agents. Each agent decides on a proper action to solve the task using multiple inputs, e.g., history of actions, interactions with its neighboring agents, and its goal. The MAS has found multiple applications, including modeling complex systems, smart grids, and computer networks. Despite their wide applicability, there are still a number of challenges faced by MAS, including coordination between agents, security, and task allocation. This survey provides a comprehensive discussion of all aspects of MAS, starting from definitions, features, applications, challenges, and communications to evaluation. A classification on MAS applications and challenges is provided along with references for further studies. We expect this paper to serve as an insightful and comprehensive resource on the MAS for researchers and practitioners in the area.

Modeling techniques used in building HVAC control systems: A review

Abstract: The appropriate application of advanced control strategies in Heating, Ventilation, and Air-conditioning (HVAC) systems is key to improving the energy efficiency of buildings. Significant advances have been made in the past decades on model development to provide better control over the energy consumption of system components while simultaneously ensuring a satisfactory indoor environment in terms of thermal comfort and indoor air quality. Yet it is an ongoing challenge to select and implement the best-suited modeling technique for improving the control strategy of HVAC systems. For the development of modeling research it is important that the building research community is informed about the role, application, merits, shortcomings and outcomes of different modeling techniques used in HVAC systems. Even though several review articles have been published on modeling techniques, the weaknesses and strengths of these modeling techniques, along with performances of developed models associated with research studies, have rarely been identified. This study presents a critical review of current modeling techniques used in HVAC systems regarding their applicability and ease of acceptance in practice and summarizes the strengths, weaknesses, applications and performance of these modeling techniques. Additionally, the performance and outcome of some of the developed models used in real world HVAC systems have been discussed. From the extensive critical review it is evident that almost every model has a major/minor shortcoming generated from assumptions, unmeasured disturbances or uncertainties in some system properties. This review aims at highlighting the shortcomings of existing application-based research on HVAC systems, and accordingly, recommendations are presented to improve the performance of building HVAC systems.