A review on simulation-based optimization methods applied to building performance analysis

Buildings all around the world consume a significant amount of energy, which is more or less one-third of the total primary energy resources. This has raised concerns over energy supplies, rapid energy resource depletion, rising building service demands, improved comfort life styles along with the increased time spent in buildings; consequently, this has shown a rising energy demand in the near future. However, contemporary buildings’ energy efficiency has been fast tracked solution to cope/limit the rising energy demand of this sector. Building energy efficiency has turned out to be a multi-faceted problem, when provided with the limitation for the satisfaction of the indoor comfort index. However, the comfort level for occupants and their behavior have a significant effect on the energy consumption pattern. It is generally perceived that energy unaware activities can also add one-third to the building’s energy performance. Researchers and investigators have been working with this issue for over a decade; yet it remains a challenge. This review paper presents a comprehensive and significant research conducted on state-of-the-art intelligent control systems for energy and comfort management in smart energy buildings (SEB’s). It also aims at providing a building research community for better understanding and up-to-date knowledge for energy and comfort related trends and future directions. The main table summarizes 121 works closely related to the mentioned issue. Key areas focused on include comfort parameters, control systems, intelligent computational methods, simulation tools, occupants’ behavior and preferences, building types, supply source considerations and countries research interest in this sector. Trends for future developments and existing research in this area have been broadly studied and depicted in a graphical layout. In addition, prospective future advancements and gaps have also been discussed comprehensively.

A review on optimized control systems for building energy and comfort management of smart sustainable buildings

Buildings consume about 41.1% of primary energy and 74% of the electricity in the U.S. Better or even optimal building energy control and operation strategies provide great opportunities to reduce building energy consumption. Moreover, it is estimated by the National Energy Technology Laboratory that more than one-fourth of the 713 GW of U.S. electricity demand in 2010 could be dispatchable if only buildings could respond to that dispatch through advanced building energy control and operation strategies and smart grid infrastructure. Energy forecasting models for building energy systems are essential to building energy control and operation. Three general categories of building energy forecasting models have been reported in the literature which include white-box (physics-based), black-box (data-driven), and gray-box (combination of physics based and data-driven) modeling approaches. This paper summarizes the existing efforts in this area as well as other critical areas related to building energy modeling, such as short-term weather forecasting. An up-to-date overview of research on application of building energy modeling methods in optimal control for single building and multiple buildings is also summarized in this paper. Different model-based and model-free optimization methods for building energy system operation are reviewed and compared in this paper. Agent based modeling, as a new modeling strategy, has made a remarkable progress in distributed energy systems control and optimization in the past years. The research literature on application of agent based model in building energy system control and operation is also identified and discussed in this paper.

Review of building energy modeling for control and operation

Optimal electrical and thermal energy management of a residential energy hub, integrating demand response and energy storage system

Advances in information and communication technologies (ICT) enable a great opportunity to develop the residential demand response that is relevant in smart grid applications. Demand response (DR) aims to manage the required demand to match the available energy resources without adding new generation capacity. Expanding the DR to cover the residential sector in addition to the industrial and commercial sectors gives rise to a wide range of challenges. This study presents an overview of the literature on residential DR systems, load-scheduling techniques, and the latest ICT that supports residential DR applications. Furthermore, challenges are highlighted and analyzed, which are likely to become relevant research topics with regard to the residential DR of smart grid. The literature review shows that most DR schemes suffer from an externality problem that involves the effect of high-level customer consumption on the price rates of other customers, especially during peak period. A recommendation for using adaptive multi-consumption level pricing scheme is presented to overcome this challenge. *Corresponding author at: Department of Electronics and Communication Engineering, Tenaga Nasional Universiti, P13-B-07-06, Sri Cempaka, Jalan Sepakat Indah 2/2 Taman Sep, Kajang 43000 Selangor, Malaysia. Tel.: +60 183262643.

A review of residential demand response of smart grid

Smart and energy-efficient buildings have recently become a trend for future building industry. The major challenge in the control system design for such a building is to minimize the power consumption without compromising the customers comfort. For this purpose, a hierarchical multiagent control system with an intelligent optimizer is proposed in this study. Four types of agents, which are switch agent, central coordinator-agent, local controller-agent, and load agent, cooperate with each other to achieve the overall control goals. Particle swarm optimization (PSO) is utilized to optimize the overall system and enhance the intelligence of the integrated building and microgrid system. A Graphical User Interface (GUI) based platform is designed for customers to input their preferences and monitor the results. Two sets of case studies are carried out and corresponding simulation results are presented in this paper.

Intelligent Multiagent Control System for Energy and Comfort Management in Smart and Sustainable Buildings

Multi-agent control system with information fusion based comfort model for smart buildings

In this paper, a new control system with an intelligent optimizer is developed which can be applied to energy and comfort management in the smart and energy-efficient buildings. Hierarchical multi-agent theory is used to build this control system, which contains agent-controllers at two levels — a central coordinator-agent at the higher level and multiple local controller-agents at the lower level. Particle Swarm Optimization (PSO) is adopted to optimize the set points of the control system during system operations. This multi-agent intelligent control system is utilized to minimize the main conflict in smart and energy-efficient buildings in terms of power consumption and customers' comfort.

Multi-agent control system with intelligent optimization for smart and energy-efficient buildings

Integration of plug-in hybrid electric vehicles into energy and comfort management for smart building

In this paper, a multi-agent based control framework is proposed for energy and comfort management in integrated smart building and micro-grid systems. This micro-grid system operates independently from the power grid, and distributed renewable resources including solar, wind, and batteries supply power to the whole building. A multi-agent control system with particle swarm optimizer (PSO) is developed in this work, which is made up of a central coordinator-agent and multiple local controller-agents. Also several load agents are utilized in emergency situations. Simulation results are presented and discussed. This intelligent control system turns out to be capable of achieving effective energy and comfort management in green building control.

Zhu Wang

Papers

Intelligent Multiagent Control System for Energy and Comfort Management in Smart and Sustainable Buildings

Multi-agent control system with information fusion based comfort model for smart buildings

Multi-agent control system with intelligent optimization for smart and energy-efficient buildings

Integration of plug-in hybrid electric vehicles into energy and comfort management for smart building

Intelligent multi-agent control for integrated building and micro-grid systems