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Dr. Yongjun Sun
Division of Building Science and Technology, City University of Hong Kong

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Research Keywords & Expertise

0 Building Energy Management
0 Zero energy building (ZEB) system design and control
0 Coordinated demand response
0 HVAC optimal and robust control
0 Renewable energy application

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Zero energy building (ZEB) system design and control
Building Energy Management
Coordinated demand response

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Journal article
Published: 16 August 2021 in Solar Energy Materials and Solar Cells
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Solar energy radiation during the midday could causes the internal temperature of building rising, thus resulting in a significant source of power consumption to run air conditioner devices for thermal comfort environment. A growing interest developed in phase change materials (PCMs) applied in building envelope owing to the prevailing energy challenges. A new type of shape-stabilized composite phase change material (CPCM) was developed by introducing a novel Na2HPO4·12H2O–K2HPO4·3H2O (DSP-PPDT) eutectic hydrated salt into super absorbent polymer (SAP), which was characterized by differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction (XRD), Scanning electron microscope (SEM). Results indicated that DSP-PPDT eutectic hydrated salts in various ratios all can be formed, among which DSP-PPDT eutectic hydrated salt with the mass fraction of PPDT at 25% had a melting temperature of 24.26 °C, making it suitable for building envelope. The results of cooling tests suggested that 2% of Na2SiO3·9H2O nucleating agent could reduce the supercooling degree of the eutectic hydrated salt to 0.79 °C. The modified eutectic hydrated salt could be stabilized in the network structure of SAP at a mass fraction of 12% through physical interaction without leakage, which melted at 24.13 °C with the enthalpy of 172.7 J/g and had enhanced thermal stability, good thermal reliability at 100 thermal cycles as well as low thermal conductivity of 0.474 W/(m·K). The good thermal performances of CPCM make it a promising candidate applied in building envelope.

ACS Style

Ting Zou; Tao Xu; Hongzhi Cui; Hongfei Tao; Huijin Xu; Xiaoqing Zhou; Qiliang Chen; Jiayu Chen; Gongsheng Huang; Yongjun Sun. Super absorbent polymer as support for shape-stabilized composite phase change material containing Na2HPO4·12H2O–K2HPO4·3H2O eutectic hydrated salt. Solar Energy Materials and Solar Cells 2021, 231, 111334 .

AMA Style

Ting Zou, Tao Xu, Hongzhi Cui, Hongfei Tao, Huijin Xu, Xiaoqing Zhou, Qiliang Chen, Jiayu Chen, Gongsheng Huang, Yongjun Sun. Super absorbent polymer as support for shape-stabilized composite phase change material containing Na2HPO4·12H2O–K2HPO4·3H2O eutectic hydrated salt. Solar Energy Materials and Solar Cells. 2021; 231 ():111334.

Chicago/Turabian Style

Ting Zou; Tao Xu; Hongzhi Cui; Hongfei Tao; Huijin Xu; Xiaoqing Zhou; Qiliang Chen; Jiayu Chen; Gongsheng Huang; Yongjun Sun. 2021. "Super absorbent polymer as support for shape-stabilized composite phase change material containing Na2HPO4·12H2O–K2HPO4·3H2O eutectic hydrated salt." Solar Energy Materials and Solar Cells 231, no. : 111334.

Journal article
Published: 24 March 2021 in Sustainable Cities and Society
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The fault detection and diagnosis (FDD) of air handling units (AHUs) serves as a major task in building operation management and energy savings. Data-driven classification methods have gained increasing popularities considering their flexibilities and effectiveness in practice. One essential challenge in developing accurate and reliable FDD classification models is the lack of sufficient labeled data. In practice, it can be highly time-consuming, labor-intensive and sometimes even infeasible to collected sufficient labeled data for all possible faulty operations. As a result, the fault detection models developed by limited and partially labeled data may not perform well in detecting any unknown or unseen faults in AHU operations. This study investigates the value of semi-supervised learning in detecting unseen faults during AHU operations. The main idea is to adopt a self-training strategy to gradually enhance the model capability by leveraging large amounts of unlabeled data. Data experiments have been designed to evaluate the unseen fault detection performance, the impacts of key semi-supervised learning parameters and the difficulties in detecting typical AHU faults. The insights obtained are valuable for the integration of data sciences with massive building operational data for smart building management.

ACS Style

Cheng Fan; Yichen Liu; Xuyuan Liu; Yongjun Sun; Jiayuan Wang. A study on semi-supervised learning in enhancing performance of AHU unseen fault detection with limited labeled data. Sustainable Cities and Society 2021, 70, 102874 .

AMA Style

Cheng Fan, Yichen Liu, Xuyuan Liu, Yongjun Sun, Jiayuan Wang. A study on semi-supervised learning in enhancing performance of AHU unseen fault detection with limited labeled data. Sustainable Cities and Society. 2021; 70 ():102874.

Chicago/Turabian Style

Cheng Fan; Yichen Liu; Xuyuan Liu; Yongjun Sun; Jiayuan Wang. 2021. "A study on semi-supervised learning in enhancing performance of AHU unseen fault detection with limited labeled data." Sustainable Cities and Society 70, no. : 102874.

Journal article
Published: 27 November 2020 in Energy Conversion and Management
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Thermoelectric generators (TEGs) with improved conversion efficiency are in great need for low-grade heat recovery. Existing studies primarily optimize the dimensionless figure of merit (ZT) of thermoelectric (TE) materials to improve TEG efficiency. However, TE material with a high ZT cannot guarantee that associated TEG has an optimal performance in practical conditions. To solve this problem, an experiment-verified model is proposed considering the effective temperature difference, optimal matching resistance and contact effect. Sensitivity analysis has been conducted to inversely identify the optimization direction of TE materials at the device level. The impacts of the key physical properties on TEG performance are systematically studied under typical operating conditions. The study results show that reducing lattice thermal conductivity is of more priority than increasing the power factor for improving TEG performance. For power factor improvements of TE materials, Seebeck coefficient optimization is found to be more important than electrical conductivity increase. Besides, the impacts of operating conditions on optimizing TE materials are also investigated. Finally, an optimization process to improve the generation performance of TE materials is proposed, which opens up a new way to lead the development of TE materials from the device and application level. The study results are helpful to effectively improve the power generation performance of TEG through the proposed optimized method of TE materials.

ACS Style

Limei Shen; Yupeng Wang; Xiao Tong; Shenming Xu; Yongjun Sun. Inverse optimization investigation for thermoelectric material from device level. Energy Conversion and Management 2020, 228, 113669 .

AMA Style

Limei Shen, Yupeng Wang, Xiao Tong, Shenming Xu, Yongjun Sun. Inverse optimization investigation for thermoelectric material from device level. Energy Conversion and Management. 2020; 228 ():113669.

Chicago/Turabian Style

Limei Shen; Yupeng Wang; Xiao Tong; Shenming Xu; Yongjun Sun. 2020. "Inverse optimization investigation for thermoelectric material from device level." Energy Conversion and Management 228, no. : 113669.

Journal article
Published: 08 September 2020 in Applied Energy
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Utilizing renewable energy to meet the energy demand, net-zero energy building (NZEB) is considered a promising solution to the worsening energy and environmental problems. Due to the intermittent and unstable characteristics of renewable energy (e.g. solar energy), NZEB needs to frequently exchange energy with the power grid. Such frequent energy interactions can impose negative impacts on the grid in terms of power balance and voltage stability. Existing studies demonstrated that there exist many influential parameters to NZEB grid interaction. However, the impacts of influential parameters have not been systematically compared and the key parameters with critical impacts are still unknown. Without knowing the key parameters, researchers may mistakenly optimize non-critical parameters, thereby leading to limited performance improvements; or they have to take parameters more than necessary into consideration, thereby causing unnecessarily high computation loads. Therefore, this study proposes a novel method to identify the key parameters affecting NZEB grid interactions. In the method, global sensitivity analysis is adopted to quantitatively compare the impacts of 24 influential parameters in three major performance aspects including over/under voltage, grid dependence and energy loss. Meanwhile, Monte-Carlo method is used to simulate the parameter uncertainties. The identified key parameters have been verified through comparing their performance improvements and computation loads. Providing an effective way to identify key parameters out of numerous ones, the study results can substantially reduce the unnecessary considerations of non-critical parameters in design optimizations. Also, the identified key parameters can be used for improving NZEB grid interaction with limited computing power requirement.

ACS Style

Yelin Zhang; Xingxing Zhang; Pei Huang; Yongjun Sun. Global sensitivity analysis for key parameters identification of net-zero energy buildings for grid interaction optimization. Applied Energy 2020, 279, 115820 .

AMA Style

Yelin Zhang, Xingxing Zhang, Pei Huang, Yongjun Sun. Global sensitivity analysis for key parameters identification of net-zero energy buildings for grid interaction optimization. Applied Energy. 2020; 279 ():115820.

Chicago/Turabian Style

Yelin Zhang; Xingxing Zhang; Pei Huang; Yongjun Sun. 2020. "Global sensitivity analysis for key parameters identification of net-zero energy buildings for grid interaction optimization." Applied Energy 279, no. : 115820.

Journal article
Published: 11 January 2020 in Applied Energy
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The wide availability of massive building operational data has motivated the development of advanced data-driven methods for building energy predictions. Existing data-driven prediction methods are typically customized for individual buildings and their performance are highly influenced by the training data amount and quality. In practice, buildings may only possess limited measurements due to the lack of advanced monitoring systems or data accumulation time. As a result, existing data-driven approaches may not present sufficient values for practical applications. A novel solution can be developed based on transfer learning, which utilizes the knowledge learnt from well-measured buildings to facilitate prediction tasks in other buildings. However, the potentials of transfer learning-based methods for building energy predictions have not been systematically examined. To address this research gap, a transfer learning-based methodology is proposed for 24-h ahead building energy demand predictions. Experiments have been designed to investigate the potentials of transfer learning in different scenarios with different implementation strategies. Statistical assessments have been performed to validate the value of transfer learning in short-term building energy predictions. Compared with standalone models, the transfer learning-based methodology could reduce approximately 15% to 78% of prediction errors. The research outcomes are useful for developing advanced transfer learning-based methods for typical tasks in building energy management. The insights obtained can help the building industry to fully realize the value of existing building data resources and advanced data analytics.

ACS Style

Cheng Fan; Yongjun Sun; Fu Xiao; Jie Ma; Dasheng Lee; Jiayuan Wang; Yen Chieh Tseng. Statistical investigations of transfer learning-based methodology for short-term building energy predictions. Applied Energy 2020, 262, 114499 .

AMA Style

Cheng Fan, Yongjun Sun, Fu Xiao, Jie Ma, Dasheng Lee, Jiayuan Wang, Yen Chieh Tseng. Statistical investigations of transfer learning-based methodology for short-term building energy predictions. Applied Energy. 2020; 262 ():114499.

Chicago/Turabian Style

Cheng Fan; Yongjun Sun; Fu Xiao; Jie Ma; Dasheng Lee; Jiayuan Wang; Yen Chieh Tseng. 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions." Applied Energy 262, no. : 114499.

Journal article
Published: 01 October 2019 in Energy
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ACS Style

Jiale Chai; Pei Huang; Yongjun Sun. Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions. Energy 2019, 185, 176 -189.

AMA Style

Jiale Chai, Pei Huang, Yongjun Sun. Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions. Energy. 2019; 185 ():176-189.

Chicago/Turabian Style

Jiale Chai; Pei Huang; Yongjun Sun. 2019. "Investigations of climate change impacts on net-zero energy building lifecycle performance in typical Chinese climate regions." Energy 185, no. : 176-189.

Journal article
Published: 02 July 2019 in Energy and Buildings
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Active supply-demand interactions in a smart grid are essential for reducing grid power imbalance which is important for the security and efficiency of power supply. A key element to the success of such interactions is the proper pricing strategy. The latest game-theory based dynamic pricing methods require information exchanges not only between the supply and demand sides, but also among individual buildings, since they make decisions for one building's demand response based on/influenced by operations of the others. However, in practical applications in which a number of buildings are considered, the latter information exchanges are extremely difficult due to the concerns of privacy, communication complexity and high computation load. Therefore, this study proposed a genetic algorithm based dynamic pricing method for improving bi-directional interactions with reduced power imbalance, which does not require information exchanges among individual buildings. In this study, at the demand side, targeting at minimizing daily electricity cost, a non-linear programming based demand response control is performed in individual buildings at a dynamic price given by the grid operator genetic algorithm optimizer. Targeting at reducing grid power imbalance, the genetic algorithm optimizer is used by the grid operator to search for a better dynamic price based on the aggregated demand response results. Such interaction will continue until the grid power imbalance cannot be further reduced. The impacts of demand elasticity are also investigated on performance improvements. The proposed pricing method can be used in practical applications to improve dynamic pricing of a smart grid for reduced grid power imbalance and thus increased operation efficiency.

ACS Style

Pei Huang; Tao Xu; Yongjun Sun. A genetic algorithm based dynamic pricing for improving bi-directional interactions with reduced power imbalance. Energy and Buildings 2019, 199, 275 -286.

AMA Style

Pei Huang, Tao Xu, Yongjun Sun. A genetic algorithm based dynamic pricing for improving bi-directional interactions with reduced power imbalance. Energy and Buildings. 2019; 199 ():275-286.

Chicago/Turabian Style

Pei Huang; Tao Xu; Yongjun Sun. 2019. "A genetic algorithm based dynamic pricing for improving bi-directional interactions with reduced power imbalance." Energy and Buildings 199, no. : 275-286.

Journal article
Published: 01 March 2019 in Energy
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Collaborations (e.g. renewable energy sharing) among nearly zero energy buildings can improve performances at cluster level. Demand response control is helpful to enable such collaborations. Existing studies have developed some dynamic pricing demand response control methods to reduce the nearly zero energy building cluster’ electricity bills and eliminate the power grid’s undesirable peaks. However, in these controls the collaborations among buildings are not allowed/enabled, since each building interacts with the grid and there is no direct interaction among buildings. Meanwhile, for performance optimizations at building cluster level, the computation costs of these non-collaborative controls are excessively high especially as a number of buildings considered. Therefore, this study proposes a collaborative demand response of nearly zero energy buildings in response to dynamic pricing for cluster-level performance improvements. Considering the building cluster as one ‘lumped’ building, in which the renewable generations, energy demands and battery capacities of individual buildings are aggregated, the collaborative control first identifies the optimal performance at cluster level in response to the dynamic pricing. Then, based on the identified optimal performance, the proposed control coordinates individual buildings’ operations using non-linear programming, thereby realizing the collaborations. For validation, the proposed collaborative demand response control is compared with a game-theory based non-collaborative demand response control. The developed control effectively reduces the cluster-level peak energy exchanges and electricity bills by 18% and 45.2%, respectively, with significant computational load reduction. This study will provide the decision makers a computation-efficient demand response control of nearly zero energy buildings which enables full collaborations and thus helps improve the performances.

ACS Style

Pei Huang; Yongjun Sun. A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level. Energy 2019, 174, 911 -921.

AMA Style

Pei Huang, Yongjun Sun. A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level. Energy. 2019; 174 ():911-921.

Chicago/Turabian Style

Pei Huang; Yongjun Sun. 2019. "A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level." Energy 174, no. : 911-921.

Journal article
Published: 13 February 2019 in Applied Energy
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The enrichment in building operation data has enabled the development of advanced data-driven methods for building energy predictions. Existing studies mainly focused on the utilization of supervised learning techniques for model development, while overlooking the significance of feature engineering. Feature engineering are helpful for reducing data dimensionality, decreasing prediction model complexity, and tackling the problem of corrupted and noisy information. Considering that each building has unique operating characteristics, it is neither practical nor efficient to manually identify features for model developments. Data-driven feature engineering methods are thus needed to ensure the flexibility and generalization of building energy prediction models. Using operation data of real buildings, this paper investigates the performance of different deep learning techniques in automatically deriving high-quality features for building energy predictions. Three types of deep learning-based features are developed using fully-connected autoencoders, convolutional autoencoders and generative adversarial networks respectively. Their potentials in building energy predictions have been exploited and compared with conventional feature engineering methods. The study validates the usefulness of deep learning in enhancing building energy prediction performance. The research results help to automate and improve the predictive modeling process while bridging the knowledge gaps between deep learning and building professionals.

ACS Style

Cheng Fan; Yongjun Sun; Yang Zhao; Mengjie Song; Jiayuan Wang. Deep learning-based feature engineering methods for improved building energy prediction. Applied Energy 2019, 240, 35 -45.

AMA Style

Cheng Fan, Yongjun Sun, Yang Zhao, Mengjie Song, Jiayuan Wang. Deep learning-based feature engineering methods for improved building energy prediction. Applied Energy. 2019; 240 ():35-45.

Chicago/Turabian Style

Cheng Fan; Yongjun Sun; Yang Zhao; Mengjie Song; Jiayuan Wang. 2019. "Deep learning-based feature engineering methods for improved building energy prediction." Applied Energy 240, no. : 35-45.

Journal article
Published: 01 February 2019 in Energy Procedia
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Sizing the nZEB systems properly is crucial for nZEBs to achieve the desired performances. The energy demand prediction uncertainties and the components’ degradation are two major factors affecting the nZEB systems sizing. The energy demand prediction has been studied by many researchers, but the impacts of degradation are still neglected in most studies. Neglecting degradation may lead to a system design that can perform as expected only in the beginning several years. This paper, therefore, proposes an uncertainty-based life-cycle performance analysis (LCPA) method to study the impacts of degradation on the nZEBs longitudinal performance. Based on the LCPA method, this study also proposes a two-stage method to enhance the nZEB system sizing. The study can enhance the designers’ understanding of the components’ degradation impacts. Case studies show that an nZEB might not achieve zero energy targets after years due to degradation. The proposed two-stage design method can effectively mitigate this problem.

ACS Style

Jiale Chai; Pei Huang; Yongjun Sun. Life-cycle analysis of nearly zero energy buildings under uncertainty and degradation impacts for performance improvements. Energy Procedia 2019, 158, 2762 -2767.

AMA Style

Jiale Chai, Pei Huang, Yongjun Sun. Life-cycle analysis of nearly zero energy buildings under uncertainty and degradation impacts for performance improvements. Energy Procedia. 2019; 158 ():2762-2767.

Chicago/Turabian Style

Jiale Chai; Pei Huang; Yongjun Sun. 2019. "Life-cycle analysis of nearly zero energy buildings under uncertainty and degradation impacts for performance improvements." Energy Procedia 158, no. : 2762-2767.

Journal article
Published: 09 November 2018 in Renewable Energy
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Collaborations among nZEBs (e.g. renewable energy sharing and battery sharing) can improve the nZEBs’ performance at the cluster level. To enable such collaborations, existing studies have developed many demand response control methods to control the operation of nZEB systems. Unfortunately, due to lack of consideration of demand prediction uncertainty, most of the demand response control methods fail to achieve the desired performance. A few methods have considered the impacts of uncertainty, but they merely perform simple and limited collaborations among nZEBs, and thus they cannot achieve the optimal performance at the cluster level. This paper, therefore, proposes a nZEB control method that enables full collaborations among nZEBs and takes account of the demand prediction uncertainty. The proposed robust control method first analyzes the demand prediction uncertainty, next optimizes the nZEB cluster operation under uncertainty, and then coordinates single nZEB’s operation using the cluster operational parameters. The performance of the robust control has been studied and compared with a deterministic control. Case studies show that the robust control can effectively increase the cluster load matching and reduce the grid interaction with the demand prediction uncertainty existed. The proposed method can achieve robust performance improvements for the nZEB cluster in practice particularly as uncertainty exists.

ACS Style

Pei Huang; Yongjun Sun. A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty. Renewable Energy 2018, 134, 215 -227.

AMA Style

Pei Huang, Yongjun Sun. A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty. Renewable Energy. 2018; 134 ():215-227.

Chicago/Turabian Style

Pei Huang; Yongjun Sun. 2018. "A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty." Renewable Energy 134, no. : 215-227.

Journal article
Published: 03 November 2018 in Applied Energy
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Collaborations among nearly zero energy buildings (nZEBs) (e.g. renewable energy sharing) can improve nZEBs’ performance at the community level. To enable such collaborations, the nZEBs need to be properly grouped. Grouping nZEBs with similar energy characteristics merely brings limited benefits due to limited collaboration existed, while grouping nZEBs with diverse energy characteristics can bring more benefits. In the planning of nZEB communities, due to the large diversity of energy characteristics and computation complexity, proper grouping that maximizes the collaboration benefits is difficult, and such a grouping method is still lacking. Therefore, this paper proposes a clustering based grouping method to improve nZEB performance. Using the field data, the grouping method first identifies the representative energy characteristics by advanced clustering algorithms. Then, it searches the optimal grouping alternative of these representative profiles that has the optimal performance. For validation, the proposed grouping method is compared with two cases (the nZEBs are either not grouped or randomly grouped) in aspects of economic costs and grid interaction. The study results demonstrate that the proposed method can effectively improve nZEBs’ performances at the community level. The propose method can provide the decision makers a means to group nZEBs, which maximize the collaboration benefits and thus assists the planning of nZEB communities.

ACS Style

Pei Huang; Yongjun Sun. A clustering based grouping method of nearly zero energy buildings for performance improvements. Applied Energy 2018, 235, 43 -55.

AMA Style

Pei Huang, Yongjun Sun. A clustering based grouping method of nearly zero energy buildings for performance improvements. Applied Energy. 2018; 235 ():43-55.

Chicago/Turabian Style

Pei Huang; Yongjun Sun. 2018. "A clustering based grouping method of nearly zero energy buildings for performance improvements." Applied Energy 235, no. : 43-55.

Journal article
Published: 18 October 2018 in Materials
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Among recent advances in electronic packaging technologies, electrically conductive adhesives (ECAs) attract most researchers' attention, as they are environment-friendly and simple to apply. ECAs also have a lower operating temperature and volume resistivity compared with conventional electronic conductive adhesives. In ECAs, the conducting fillers play a significant role in improving conductivity and strength. In this work, as filler additives, the silver nanowires/graphene nanocomposites (AgNWs-GNs) were successfully fabricated via a facile self-assembly method. The characteristics of the as-prepared nanocomposites were evaluated by FTIR (Fourier Transform infrared spectroscopy), XRD (X-ray Diffraction), XPS (X-ray photoelectron spectroscopy), TEM (Transmission electron microscope) and Raman tests, demonstrating a successful synthesis process. Different amounts of AgNWs-GNs were used as additives in micron flake silver filler, and the effects of AgNWs-GNs on the properties of ECAs were studied. The results suggested that the as-synthesized composites can significantly improve the electrical conductivity and shear strength of ECAs. With 0.8% AgNWs/GNs (AgNWs to GO (Graphite oxide) mass ratio is 4:1), the ECAs have the lowest volume resistivity of 9.31 × 10-5 Ω·cm (95.4% lower than the blank sample without fillers), while with 0.6% AgNWs/GNs (AgNWs to GO mass ratio is 6:1), the ECAs reach the highest shear strength of 14.3 MPa (68.2% higher than the blank sample).

ACS Style

Tao Xu; Jiayu Chen; Wenhui Yuan; Yinhua Liu; Yongjun Sun; Huijun Wu; Xiaoqing Zhou. Self-Assembly Synthesis of Silver Nanowires/Graphene Nanocomposite and Its Effects on the Performance of Electrically Conductive Adhesive. Materials 2018, 11, 2028 .

AMA Style

Tao Xu, Jiayu Chen, Wenhui Yuan, Yinhua Liu, Yongjun Sun, Huijun Wu, Xiaoqing Zhou. Self-Assembly Synthesis of Silver Nanowires/Graphene Nanocomposite and Its Effects on the Performance of Electrically Conductive Adhesive. Materials. 2018; 11 (10):2028.

Chicago/Turabian Style

Tao Xu; Jiayu Chen; Wenhui Yuan; Yinhua Liu; Yongjun Sun; Huijun Wu; Xiaoqing Zhou. 2018. "Self-Assembly Synthesis of Silver Nanowires/Graphene Nanocomposite and Its Effects on the Performance of Electrically Conductive Adhesive." Materials 11, no. 10: 2028.

Journal article
Published: 08 September 2018 in Energy
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Due to inherent differences in building usage and system configuration, NZEBs frequently show different sufficiency of renewable energy at same moments, thereby providing chances of renewable-energy-sharing among NZEBs. Conventional controls are developed for NZEB performance improvements at single building level while potential collaborations (renewable energy sharing) between NZEBs are rarely considered. For this reason, they are unable to achieve optimized results at building group level. This study thus proposes a new collaborative control in which renewable energy sharing are realized among NZEBs. Adopting different objective functions, case studies have been conducted to compare the proposed control with a conventional one in two aspects, i.e. operation cost and grid friendliness. The study results have shown that the proposed collaborative control is able to achieve performance improvements through renewable energy sharing among NZEBs. The proposed collaborative control can be implemented in practice to realize renewable energy sharing among NZEBs and thus improve the cost effectiveness and grid friendliness at building group level.

ACS Style

Cheng Fan; Gongsheng Huang; Yongjun Sun. A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level. Energy 2018, 164, 536 -549.

AMA Style

Cheng Fan, Gongsheng Huang, Yongjun Sun. A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level. Energy. 2018; 164 ():536-549.

Chicago/Turabian Style

Cheng Fan; Gongsheng Huang; Yongjun Sun. 2018. "A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level." Energy 164, no. : 536-549.

Journal article
Published: 01 September 2018 in Energy
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ACS Style

Pei Huang; Hunjun Wu; Gongsheng Huang; Yongjun Sun. A top-down control method of nZEBs for performance optimization at nZEB-cluster-level. Energy 2018, 159, 891 -904.

AMA Style

Pei Huang, Hunjun Wu, Gongsheng Huang, Yongjun Sun. A top-down control method of nZEBs for performance optimization at nZEB-cluster-level. Energy. 2018; 159 ():891-904.

Chicago/Turabian Style

Pei Huang; Hunjun Wu; Gongsheng Huang; Yongjun Sun. 2018. "A top-down control method of nZEBs for performance optimization at nZEB-cluster-level." Energy 159, no. : 891-904.

Journal article
Published: 25 August 2018 in Energy
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Nearly zero energy buildings (nZEBs) are considered as a promising solution to mitigate the energy and environmental problems. A proper sizing of the nZEB systems (e.g. HVAC systems, PV panels, wind turbines and batteries) is essential for achieving the desirable level of thermal comfort, energy balance and grid dependence. Parameter uncertainty, component degradation and maintenance are three crucial factors affecting the nZEB system performances and should be systematically considered in system sizing. Until now, there are some uncertainty-based design methods been developed, but most of the existing studies neglect component degradation and maintenance. Due to the complex impacts of degradation and maintenance, proper sizing of nZEB systems considering multiple criteria (i.e. thermal comfort, energy balance and grid dependence) is still a great challenge. This paper, therefore, proposes a robust design method of nZEB systems using genetic algorithm (GA) which takes into account the parameter uncertainty, component degradation and maintenance. The nZEB life-cycle cost is used as the fitness function, and the user’ performance requirements on thermal comfort, energy balance and grid dependence are defined as three constraints. This study can help improve the designers’ understanding of the impacts of uncertainty, degradation, and maintenance on the nZEB life-cycle performances. The proposed method is effective in minimizing the nZEB life-cycle cost through designing the robust optimal nZEB systems sizes and planning the optimal maintenance scheme, meanwhile satisfying the user specified constraints on thermal comfort, energy balance, and grid dependence during the whole service life.

ACS Style

Pei Huang; Gongsheng Huang; Yongjun Sun. A robust design of nearly zero energy building systems considering performance degradation and maintenance. Energy 2018, 163, 905 -919.

AMA Style

Pei Huang, Gongsheng Huang, Yongjun Sun. A robust design of nearly zero energy building systems considering performance degradation and maintenance. Energy. 2018; 163 ():905-919.

Chicago/Turabian Style

Pei Huang; Gongsheng Huang; Yongjun Sun. 2018. "A robust design of nearly zero energy building systems considering performance degradation and maintenance." Energy 163, no. : 905-919.

Journal article
Published: 01 August 2018 in Applied Energy
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The development of information technologies has enabled real-time monitoring and controls over building operations. Massive amounts of building operational data are being collected and available for knowledge discovery. Advanced data analytics are urgently needed to fully realize the potentials of big building operational data in enhancing building energy efficiency. The rapid development of data mining has provided powerful tools for extracting insights in various knowledge representations. Gradual pattern mining is a promising technique for discovering useful patterns from building operational data. The knowledge discovered is represented as gradual relationships, i.e., “the more/less A, the more/less B”. It can bring special interests to building energy management by highlighting co-variations among numerical building variables. This study investigated the usefulness of gradual pattern mining for building energy management. A generic methodology was proposed to ensure the quality and applicability of the knowledge discovered. The methodology was validated through a case study. The results showed that the methodology could successfully extract valuable insights on building operation characteristics and provide opportunities for building energy efficiency enhancement.

ACS Style

Cheng Fan; Yongjun Sun; Kui Shan; Fu Xiao; Jiayuan Wang. Discovering gradual patterns in building operations for improving building energy efficiency. Applied Energy 2018, 224, 116 -123.

AMA Style

Cheng Fan, Yongjun Sun, Kui Shan, Fu Xiao, Jiayuan Wang. Discovering gradual patterns in building operations for improving building energy efficiency. Applied Energy. 2018; 224 ():116-123.

Chicago/Turabian Style

Cheng Fan; Yongjun Sun; Kui Shan; Fu Xiao; Jiayuan Wang. 2018. "Discovering gradual patterns in building operations for improving building energy efficiency." Applied Energy 224, no. : 116-123.

Journal article
Published: 06 July 2018 in Applied Energy
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Properly treating uncertainty is critical for robust system sizing of nearly/net zero energy buildings (ZEBs). To treat uncertainty, the conventional method conducts Monte Carlo simulations for thousands of possible design options, which inevitably leads to computation load that is heavy or even impossible to handle. In order to reduce the number of Monte Carlo simulations, this study proposes a response-surface-model-based system sizing method. The response surface models of design criteria (i.e., the annual energy match ratio, self-consumption ratio and initial investment) are established based on Monte Carlo simulations for 29 specific design points which are determined by Box-Behnken design. With the response surface models, the overall performances (i.e., the weighted performance of the design criteria) of all design options (i.e., sizing combinations of photovoltaic, wind turbine and electric storage) are evaluated, and the design option with the maximal overall performance is finally selected. Cases studies with 1331 design options have validated the proposed method for 10,000 randomly produced decision scenarios (i.e., users’ preferences to the design criteria). The results show that the established response surface models reasonably predict the design criteria with errors no greater than 3.5% at a cumulative probability of 95%. The proposed method reduces the number of Monte Carlos simulations by 97.8%, and robustly sorts out top 1.1% design options in expectation. With the largely reduced Monte Carlo simulations and high overall performance of the selected design option, the proposed method provides a practical and efficient means for system sizing of nearly/net ZEBs under uncertainty.

ACS Style

Sheng Zhang; Yongjun Sun; Yong Cheng; Pei Huang; Majeed Oladokun; Zhang Lin. Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty. Applied Energy 2018, 228, 1020 -1031.

AMA Style

Sheng Zhang, Yongjun Sun, Yong Cheng, Pei Huang, Majeed Oladokun, Zhang Lin. Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty. Applied Energy. 2018; 228 ():1020-1031.

Chicago/Turabian Style

Sheng Zhang; Yongjun Sun; Yong Cheng; Pei Huang; Majeed Oladokun; Zhang Lin. 2018. "Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty." Applied Energy 228, no. : 1020-1031.

Original articles
Published: 25 June 2018 in Journal of Building Performance Simulation
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Cooling loss during transmission from cooling sources (chillers) to cooling end-users (conditioned zones) is prevalent in HVAC systems. At the HVAC design stage, incomplete understanding of the cooling loss may lead to improper sizing of HVAC systems, which in turn may result in additional energy consumption and economic cost (if oversized) or lead to inadequate thermal comfort (if under-sized). For HVAC system sizing or retrofit, there is a lack of study of uncertainties associated with the maximum cooling loss of HVAC systems although uncertainties in predicting building maximum cooling load have been studied by many researchers. This paper, therefore, proposes a study to investigate the uncertainties associated with the major parameters in predicting the maximum cooling loss in HVAC piping networks using the Bayesian Markov Chain Monte Carlo method. Prior information of those uncertainties combined with available in-situ data, is implemented to produce more informative posterior descriptions of the uncertainties. To facilitate the application, uncertain parameters are categorized into specific and generic types. The posterior information gathered for the specific parameters can be used in retrofit analysis, whereas that acquired for the generic parameters can be referred to in new HVAC system design. Details of the proposed methodology are illustrated by applying it to a real HVAC system.

ACS Style

Pei Huang; Godfried Augenbroe; Gongsheng Huang; Yongjun Sun. Investigation of maximum cooling loss in a piping network using Bayesian Markov Chain Monte Carlo method. Journal of Building Performance Simulation 2018, 12, 117 -132.

AMA Style

Pei Huang, Godfried Augenbroe, Gongsheng Huang, Yongjun Sun. Investigation of maximum cooling loss in a piping network using Bayesian Markov Chain Monte Carlo method. Journal of Building Performance Simulation. 2018; 12 (2):117-132.

Chicago/Turabian Style

Pei Huang; Godfried Augenbroe; Gongsheng Huang; Yongjun Sun. 2018. "Investigation of maximum cooling loss in a piping network using Bayesian Markov Chain Monte Carlo method." Journal of Building Performance Simulation 12, no. 2: 117-132.

Articles
Published: 19 April 2018 in Science and Technology for the Built Environment
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Real-time optimal control is widely adopted in central air-conditioning systems to improve the building energy management efficiency by adjusting system controllable variables according to system operating conditions. In time-driven optimal control, optimization actions are triggered periodically, which may not be efficient when the operating conditions of central air-conditioning systems are significantly irregular. To overcome this limitation, event-driven optimal control was proposed, in which optimization actions are triggered by “events” instead of a “clock”. In event-driven optimal control, the definition of events are critical because “events” will directly affect the control efficiency and the online computational efficiency. In current literature, however, very few studies can be found on how to systematically establish a suitable event space for air-conditioning systems. This paper therefore presents a knowledge-based method for establishing event space. Case studies that were carried out in a typical commercial air-conditioning system is used to demonstrate the proposed method and its effectiveness, where real measured load and weather data were used.

ACS Style

Junqi Wang; Qing-Shan Jia; Gongsheng Huang; Yongjun Sun. Event-driven optimal control of central air-conditioning systems: Event-space establishment. Science and Technology for the Built Environment 2018, 24, 839 -849.

AMA Style

Junqi Wang, Qing-Shan Jia, Gongsheng Huang, Yongjun Sun. Event-driven optimal control of central air-conditioning systems: Event-space establishment. Science and Technology for the Built Environment. 2018; 24 (8):839-849.

Chicago/Turabian Style

Junqi Wang; Qing-Shan Jia; Gongsheng Huang; Yongjun Sun. 2018. "Event-driven optimal control of central air-conditioning systems: Event-space establishment." Science and Technology for the Built Environment 24, no. 8: 839-849.