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ScienceDirect Procedia Engineering 169 (2016) 100 – 107

4th International Conference on Countermeasures to Urban Heat Island (UHI) 2016

Intelligent Control System Integration and Optimization for Zero Energy Buildings to Mitigate Urban Heat Island Zheng Zhenga,b,*, LixiongWanga, Nyuk HienWongb a

Tianjin Key Laboratory of Architectural Physics and Environmental Technology, School of Architecture, Tianjin University. Tianjin 300072, China b Department of Building, School of Design and Environment, National University of Singapore. Singapore 117566

Abstract The increased use of renewable energy sources in buildings has resulted in the development of Zero Energy Buildings(ZEB). To assess the gaps for ZEB system integration and performance optimization,this paper summarizes a study undertaken to reveal potential challenges and opportunities for intelligent control system of ZEB, and comprehensively reviews current trends in building system performance optimization. The findings indicate a breakthrough of 3M (Micro-grid, Multi-agent, Multiobjective)system of ZEB for system integration and performance optimization and a concept of ZEB to Urban Heat Island(UHI) mitigation based on Smart Grid. © 2016 2016The TheAuthors. Authors. Published by Elsevier Ltd. is an open access article under the CC BY-NC-ND license Published by Elsevier Ltd. This (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility ofthe organizing committee of the 4th IC2UHI2016. Peer-review under responsibility of the organizing committee of the 4th IC2UHI2016 Keywords: Urban heat island; Zero energy buildings; Intelligent control system; Multi-agent system; Multi-objective optimization

1. Introduction Known as the 21st Conference of the Parties(COP21) in Paris at the end of 2015, the meeting ended with a deal among 195 nations to curb global temperature increases by slowing the rise in greenhouse gas levels. The deal emphasizes the importance of developing and spreading new low-emissions technology. At the conference, 20 countries including the United States, China, and a number of European countries vowed to double clean energy research and development spending over 5 years. In fact, concerted efforts in the building sector which accounts for

* Corresponding author. Tel.: +86-22-27404491; fax: +86-22-27890420. E-mail address: [email protected]

1877-7058 © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of the 4th IC2UHI2016

doi:10.1016/j.proeng.2016.10.012

Zheng Zheng et al. / Procedia Engineering 169 (2016) 100 – 107

about 40% of the global primary energy consumption nowadays, have been carried out to improve sustainability of energy supply and to achieve UHI mitigation.The increased use of renewable energy sources in buildings has resulted in the development of ZEB, which are a promising approach to reduce fossil energy consumption. Building intelligent control system, due to its interactive and autonomous properties is considered a key element for improving coordination of ZEB operations, and eventually influencing total building performance and integration. There are a few review papers in the literature addressed different aspects related to high energy performance building integration and optimization. Regarding the building control and operation strategies, Shaikh et al. [1] reviewed intelligent control systems for energy and comfort management in smart energy buildings. Labeodan et al. [2] reviewed the application of multi-agent systems(MAS) in building operations for coordination of various building processes and buildings interaction with the smart-grid. Regarding the energy systems, Upadhyay et al. [3] reviewed integration configurations, storage options, sizing methodologies and system control of integrated renewable energy system based power generation. Regarding the optimization method, Lu et al. [4] reviewed design optimization and optimal control and standalone nearly/net ZEB. However, advanced control strategies of ZEB based on a combination of Artificial Intelligent (AI) technology and intelligent algorithm are not straight-forward, since ZEB is a dynamic system with high complexity, and integration and optimization of energy system for ZEB involving energy generation system, energy storage system, energy consumption system and contact with Smart Grid(SG). Previous literature also rarely presented a comprehensive review on the effects of multi-agent based control system, integrating optimization techniques for ZEB. Discussion of these underlying issues is significant to develop the proper methods which could deal with the integration and optimization of buildings with a holistic view, ensuring the aim of ZEB is accomplished with a highlevel total building performance. Therefore, this section introduces the background of energy and environment and up-to-date development of ZEB, and identifies the research problem within the building integration and optimization community. The rest of this paper is organized as follows. The second section reviews ZEB system integration for performance, and illustrated related studies, methods and tools to support it. Multi-objective optimization for ZEB based on intelligent algorithm are summarized in Sections 3. Section 4 proposes and discusses the concept of ZEB to UHI mitigation based on SG. The final section concludes the major findings and indicates the future perspective. 2. Zero Energy Buildings System Integration for Performance Smart micro-grid is an ad-hoc integration of complementary components, subsystems, and functions under the pervasive control of a highly intelligent and distributed management command-and-control system [5]. There are three main components in the micro-grid of ZEB, including: Distributed Generation(DG), Demand Side Management(DSM), intelligent Zero Energy Buildings System (iZEBS). 2.1. Distributed generation Renewable energy sources are one key enabler to decrease greenhouse gas (GHG) emission and to cope with the anthropogenic heat emissions. Buildings integrated renewable energy system is a common and valid DG, which is generated or stored by a variety of grid-connected devices referred to as distributed energy resources. With regards to ZEB, the mismatch between the energy demand and energy supply is one of the major problems, and high penetration levels from renewable energy sources in power system also need a high degree of flexibility in intelligent control system. Presently, about the researches of control and management for Integrated Renewable Energy System (IRES) have been used as solar assisted thermal load considering renewable energy and price uncertainty [6], sustainable hybrid micro-generation systems [7], grid tied photovoltaic (PV)-diesel-battery hybrid system powering heat pump water heater [8], and PV-equipped interconnected micro-grids [9]. In terms of case studies, Orehounig et al. [10] towards an energy sustainable community, integrated decentralized energy systems rely on local IRES and case studyresults show that an energy sustainability (ratio of energy demand covered by renewable) of 83% and a GHG emissions reduction of 86% can be achieved.

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2.2. Demand side management DSM is proposed to deal with the demand side of the grid and target at energy end-users [11]. Its aim is to reduce buildings operating cost and energy consumption through real-time monitoring of electricity consumption patterns and price, and actively managing how appliances consume energy. Recently DSM application researches in building sector focus on energy and indoor thermal comfort management, including: Thavlov et al. [12] presents a method for utilization of flexible demand in the low-voltage distribution system using the thermal mass of a building to defer power consumption from electric space heating. Sivaneasan et al. [13] proposed an intelligent preemptive DSM using the Building Management System (BMS) to ensure contracted capacity or demand limit is not exceeded and at the same time reduce energy consumption in buildings. In fact, as a representative and feasible technical system of DSM, BMS play a highly significant role, because it continuously contributes towards both energy consumption management and provision of an indoor thermal comfort for habitants. Recently, BMS usually allows the functionalities that support interactions with the various components of smart micro-grid. Such as: Korkas et al. [14] presented a simulation-based approach of intelligent energy and thermal comfort management in grid-connected micro-grids with heterogeneous occupancy schedule; Arboleya et al. [15] analyzed design, architecture and implementation of BMS in a SG. 2.3. Intelligent zero energy buildings system In order to satisfy the multiple objectives such as: energy efficiency and cost-effective of building operations, high comfort level and indoor air quality, etc. simultaneously in the built environment of ZEB, control of the actuators of shading system, ventilation system, auxiliary heating/cooling system, household appliances, etc. effectively and intelligently demand an advanced control system. x Intelligent control technologies The increasing complexity of BMS integrated with IRES requires essentially more intelligent control and scheduling strategy to manage the interaction with SG and provide the information and communication among various components of ZEB. The main research trends in the field of advanced control systems were emerged. (i) Learning based methods including artificial intelligence, fuzzy systems and neural networks-fuzzy with conventional controls, adaptive fuzzy neural network systems, etc. Yordanova et al. [16] developed a model-free design method for a two-variable Proportional Integral (PI) fuzzy controller for temperature and humidity control that ensures indoor comfort and reduces energy consumption by supervisory fuzzy tuning. However, due to the complexity of BMS and uncertainty of IRES, these systems using learning based method usually turns out to be complicated and time-consuming in the parameterization[17]. (ii) Model based predictive control (MPC) technique, which follows the principles of the classical controls. In this scenario, MPC have been admirably applied to efficient management of energy distribution in buildings by Bruni et al. [18], and design-build-operate energy information modeling infrastructure [19] and develop a nonlinear programming algorithm which is proposed to optimize the scheduling of the energy systems under day-ahead electricity pricing [20]. However, the MPC approach does not have any attention due to its significant computational requirements and unnecessarily complex of the required model identification. (iii) Agent based control systems. Currently, the agent based and contribution ratio controllers are getting more popular among researchers due to their particular assigned task capabilities. Therefore, the main discussion of this section is to survey state of the art multi-agent control systems in buildings that have been recently developed. For instance, agent based modeling of energy networks [21] as well as BMS based on MAS [22]. But it should be noted that the overlapping between the above categories is unavoidable. x MAS-based zero energy building system The modern approach to AI is centered around the concept of a rational agent. An agent is anything that can perceive its environment through sensors and act upon that environment through actuators. Agent technologies be applied into the building environment to control environmental parameters and solve possible conflicts arising between energy efficiency and user's satisfaction. Generally, an agent is defined as software or hardware entity that is situated in a certain environment and is able to autonomously react to changes in that environment. The design of


a MAS for building comprises multiple agents collaborating in a building environment [23]. MAS are particularly favoured by researchers in building because of this distinctive advantage, as well as others, i.e. providing the modular and open structure, self-adaptive capabilities, self-organization and high-level transparency [24]. Leveraging these functions, MAS have been proposed for building management system. MAS provide a valuable framework for intelligent control system to learn building and occupancy trends, negotiate energy resources, and react to real-time environmental conditions. MAS have been used to coordinate to a Gaussian adaptive resonance theory map for building intelligent heat management system [24]. They have also been used to manage micro-grid or local renewable energy supply systems [25], To improving sustainability of hybrid energy systems, Colson et al. [26] first explored formulations of storage system round-trip efficiency and operational cost, along with a model that can be determined from manufacturer data sheets and used in a real-time simulation environment for evaluation of these objectives. And then Colson et al. [27] highlighted the development and implementation of an MAS suitable for hybrid and micro-grid system applications, as well as presenting an important discussion about the tradeoffs associated with multi-objective design for power management. These systems rely on integration with facility systems and appliances through actuators and sensors.

Fig. 1 An architecture of the MAS for building intelligent control system[23].

x System architecture of MAS-based zero energy building system MAS requires the splitting of a huge complex problem into several sub-problems, which can be coped with by their representative agents; therefore, this part through analyzing an example of a hierarchical system architecture of MAS, illustrates how agents’ interaction with each other to perform the control tasks of actuators. Usually, there are two types of basic agents for different functions and purposes, including the central agent, the local agent [28]. Besides, swift agent and/or load agent etc. could be selected according to the demands of buildings.The central agent is one of the key elements in the overall control system. Cooperating with other agents, based on the outdoor environmental information, electricity grid situation and the customer preference, it is responsible for improving the indoor comfort level as well as efficiently dispatching the power to the lower-level agents.The local agents are developed which are distributed in each local subsystems, e.g. lighting agent, shading agent, etc.

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3. Intelligent Zero Energy Buildings System Performance Optimization 3.1. Building performance optimization There are increasing researches of computational optimization methods to be applied in the field of architectural, engineering and construction industry. Building performance optimization(BPO) aims at the selection of the optimal solutions from a set of available alternatives for a given design or control problem, according to a set of performance criteria. Such criteria are expressed as mathematical functions, called objective functions. Optimization is a process that searches for the optimal solution with respect to the objective functions to be maximized or minimized, possibly subjected to some constraints of the dependent variables. In building section, architects or engineering researchers continuously try to optimize systems, whether to minimize cost and energy consumption or alternatively, to maximize output, efficiency, profit and performance. 3.2 Optimization objectives and variables Generally, if the optimization problem aims at minimizing a single objective, it is called single objective optimization, otherwise if the objectives are more than one, it is called multi-objective optimization(MOO). Although, there are some researches that resolved the single objective optimization problem, which include: minimize initial investment cost [29], net-zero carbon impact [30], building heating/cooling loads [31] etc., a multiobjective approach seems more relevant than a single objective. On account of complexity for practical building system, the research emphasis gradually turned to MOO. Moreover, objective functions are equally significant and probably in conflict for the MOO. In addition, no single optimal solution is common for all the objectives. Therefore, the aim of MOO is to find a Pareto optimal solution set and the solutions set with high diversity level, rather than a single solution. MOO is proposed to instantaneously deal with multiple objective functions, probably in contradiction with each other, such as the minimum energy consumption vs. the maximum comfort(i.e., thermal [32], air quality [33], etc., may include individually or in combination) and vs. the tariff costs [34], vs. renewable energy trade-offs [35], vs. GHG emissions [36] etc. Before conducting an optimization search, the input design or control variables should be included in the optimization search, and a sensitivity analysis must be performed to identify which inputs have the largest impact on an objective. In the recent years, several studies applied MOO techniques in order to optimize a specific aspect of the building design and control. x Building envelope design and retrofit resolution [30,34]. x Heating, Ventilation and Air Conditioning (HVAC) system control for nearly ZEB [33]. x DG, hybrid renewable energy system and Combined Cooling, Heating and Power (CCHP) [36,37]. x BMS[32,38]. 3.3 Optimization algorithms and simulation tools The popular optimization methods for solving MOO problems are generally classified into three categories: enumerative algorithms, deterministic algorithms, stochastic algorithms. Since the first two algorithms are computationally expensive or continuous and derivable properties, they are not suitable for ZEB performance management system with highly constrained characteristics and multi-objective functions. The evolutionary algorithms are the most efficient stochastic algorithm. Recently, there are an increasing interest in using evolutionary algorithms(EA) such as Genetic Algorithm(GA), Particle Swarm Optimization(PSO) etc. for MOO of building control systems. The GA consider many points in the search space simultaneously, not a single point, thus they have a reduced chance of converging to local minimum, in which other algorithms may end up. The EA with the Pareto concept are used widely in energy and buildings studies [35,39]. Moreover, the elitist non-dominated sorting GA (NSGA-II) seems to be the most efficient GA. The NSGAII is implemented to find trade-off relations between energy consumption and investment cost or thermal comfort level of buildings [40].


With the development of control systems, researchers have tried to negotiate between the conflicting challenges in buildings. This has let the researchers to have a certain simulation platform to evaluate and analyze control system optimization strategies. There are various simulation tools available, including: MATLAB[34], EnergyPlus [40], GenOpt [33] and combination between them. 4. Zero Energy Buildings to Mitigate Urban Heat Island From the perspective of point and line to plane, utilizing the Smart Grid, ZEB could interconnect each other based on 3M system to monitor the ZEB of urban scale and mitigate urban microclimate and UHI effect eventually. 4.1 From ZEB to urban scale The intelligent control system provides substantial enhancement in energy efficiency and cost-effective, indoor comfort and air quality of ZEB. For building system integration for performance, Klein et al. [41] implemented a multi-agent comfort and energy system and achieved a 12% reduction in energy consumption and a 5% improvement in occupant comfort as compared to the baseline control. For total building performance optimization, Shaikh et al. [42] developed intelligent control system in combination with stochastic optimization. The corresponding case study simulations presented an energy efficiency of 31.6% has been achieved with an 8.1% improvement of comfort index using the hybrid multi-objective GA technique. For GA using multi-objectives in a multi-agent system, in the industrial field, Cardon et al. [43] first investigated GA using multi-objectives in a MAS to resolve a job-shop scheduling problem corresponding to an industrial problem. Based on the above discussion, this paper attempts to propose a concept of 3M iZEBS and using multi-objective stochastic algorithm to optimize the integrated system through summarizing building system integration and total building performance optimization technologies. However, ultimately one of common and substantial objectives of the intelligent control system for ZEB is energy efficiency, in spite of energy generation from renewable energy resource. Actually, many building projects have been presented as having net-zero energy performance, and along with the further development of the energy efficiency and renewable resources application technologies, net-positive energy buildings will become more and more common firstly which shifts to the maximization of energy performance in a system-based approach [44]. Moreover, the vision of ZEB will become more and more widely up to community, district, urban. [45] presented a conception of high performance Zero Energy Cities Planning. 4.2 ZEB to mitigate Urban Heat Island based on Smart Grid As we know, Smart Grid includes a variety of operational and energy measures including smart meters, smart appliances, renewable energy resources and energy efficiency resources, which enables these objects to collect and exchange data. The interconnection of these ZEB using SG is conceived in this paper, and is expected to usher in automation in nearly all fields, and expanding to the urban areas. Using the SG technology sense and control ZEB remotely across existing network infrastructure, and creating opportunities for more-direct integration between the physical world and computer-based systems, and resulting in improved building energy efficiency and urban energy efficiency. So far, in the context of SG, distributed monitoring and actuation through wireless sensor and actuator networks etc. technologies were fundamental to control the energy usage in buildings [46]. Moreno et al. [47] proposed a smart building management system. As we know, the two main causes of UHI are the increase in anthropogenic heat emission and decrease of vegetation and water in the urban. The building industrial which accounts for about 40% of the global primary energy consumption at the beginning of this paper have been mentioned; therefore, the heat production of building construction and operation is the main part of anthropogenic heat emission of the whole urban area, and as urbanization continues to improve, the main building energy consumption is in the urban area. Therefore, the relationship between building operation and urban microclimate influences each other-building energy and comfort vs. UHI and climate change. However, the key of the whole conception is that the implementation of ZEB performance integration and optimization to realize the intelligent monitor and control the utilization of SG connecting them from the perspective of urban scale.

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5. Conclusions This paperreviews related studies, methods and tools to support ZEB intelligent control system integration and performance optimization. A concept of 3M(Micro-grid, Multi-agent, Multi-objective)system and using multiobjective optimization algorithm to optimize the integrated system is proposed in this paper. And this system in the context of SG, distributed monitoring and actuation through wireless sensor and actuator networks etc. technologies were fundamental to control the energy usage in buildings which could achieve UHI mitigation. The applicability of current intelligent control system of ZEB still needs research efforts with intelligent reasoning and coordination to deal with the dynamic input and distributed controls. Then, with improving technology, the concept of "Internet of Everything" must be true ultimately. Simultaneously, a large and increasing amount of data is now being obtained from a huge variety of non-traditional sources. The great potential of Cloud Computing technology as an analysis tool of data is strongly tempered. More importantly, Various other artificial intelligent techniques, such as more intelligent agent to improve the total zero energy building performance by using smart technologies and standardizing instrumentation need to be future research objectives. Acknowledgments This work was supported by a grant from the National Natural Science Foundation of China No. 513300246; and Chinese Scholarship Council (The State Scholarship Study Abroad Program for Graduate Studies at The China National University of Construction). References [1] Shaikh, P.H., N.B.M. Nor, et al., Intelligent multi-objective control and management for smart energy efficient buildings. International Journal of Electrical Power & Energy Systems, 2016. 74: p. 403-409. [2] Labeodan, T., K. Aduda, et al., On the application of multi-agent systems in buildings for improved building operations, performance and smart grid interaction - A survey. Renewable & Sustainable Energy Reviews, 2015. 50: p. 1405-1414. [3] Upadhyay, S. and M.P. Sharma, A review on configurations, control and sizing methodologies of hybrid energy systems. Renewable & Sustainable Energy Reviews, 2014. 38: p. 47-63. [4] Lu, Y., S. Wang, et al., Design optimization and optimal control of grid-connected and standalone nearly/net zero energy buildings. Applied Energy, 2015. 155: p. 463-477. [5] Farhangi, H., The Path of the Smart Grid. Ieee Power & Energy Magazine, 2010. 8(1): p. 18-28. [6] Nguyen, H.T., D.T. Nguyen, et al., Energy Management for Households With Solar Assisted Thermal Load Considering Renewable Energy and Price Uncertainty. Ieee Transactions on Smart Grid, 2015. 6(1): p. 301-314. [7] Yanine, F.F., F.I. Caballero, et al., Homeostatic control, smart metering and efficient energy supply and consumption criteria: A means to building more sustainable hybrid micro-generation systems. Renewable & Sustainable Energy Reviews, 2014. 38: p. 235-258. [8] Sichilalu, S.M. and X.H. Xia, Optimal energy control of grid tied PV-diesel-battery hybrid system powering heat pump water heater. Solar Energy, 2015. 115: p. 243-254. [9] Baldi, S., A. Karagevrekis, et al., Joint energy demand and thermal comfort optimization in photovoltaic-equipped interconnected microgrids. Energy Conversion and Management, 2015. 101: p. 352-363. [10] Orehounig, K., G. Mavromatidis, et al., Towards an energy sustainable community: An energy system analysis for a village in Switzerland. Energy and Buildings, 2014. 84: p. 277-286. [11] Reynders, G., T. Nuytten, et al., Potential of structural thermal mass for demand-side management in dwellings. Building and Environment, 2013. 64: p. 187-199. [12] Thavlov, A. and H.W. Bindner, Utilization of Flexible Demand in a Virtual Power Plant Set-Up. Ieee Transactions on Smart Grid, 2015. 6(2): p. 640-647. [13] Sivaneasan, B., K.N. Kumar, et al., Preemptive Demand Response Management for Buildings. Ieee Transactions on Sustainable Energy, 2015. 6(2): p. 346-356. [14] Korkas, C.D., S. Baldi, et al., Intelligent energy and thermal comfort management in grid-connected microgrids with heterogeneous occupancy schedule. Applied Energy, 2015. 149: p. 194-203. [15] Arboleya, P., C. Gonzalez-Moran, et al., Efficient Energy Management in Smart Micro-Grids: ZERO Grid Impact Buildings. Ieee Transactions on Smart Grid, 2015. 6(2): p. 1055-1063. [16] Yordanova, S., D. Merazchiev, et al., A Two-Variable Fuzzy Control Design With Application to an Air-Conditioning System. Ieee Transactions on Fuzzy Systems, 2015. 23(2): p. 474-481. [17] Schmelas, M., T. Feldmann, et al., Adaptive predictive control of thermo-active building systems (TABS) based on a multiple regression algorithm. Energy and Buildings, 2015. 103: p. 14-28.

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