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Dr. Zhengwei Li
College of Mechanical Engineering, Tongji University, Shanghai 200092, China

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Research Keywords & Expertise

0 Green Building
0 Renewable Energy Technologies
0 Energy Efficiency in Buildings
0 Refrigeration and air conditioning
0 Building system control and operation

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Energy Efficiency in Buildings

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Research article
Published: 19 August 2021 in Building Simulation
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Air conditioning water systems account for a large proportion of building energy consumption. In a pressure-controlled water system, one of the key measures to save energy is to adjust the differential pressure setpoints during operation. Typically, such adjustments are based either on certain rules, which rely on operator experience, or on complicated models that are not easy to calibrate. In this paper, a data-driven control method based on reinforcement learning is proposed. The main idea is to construct an agent model that adapts to the researched problem. Instead of directly being told how to react, the agent must rely on its own experiences to learn. Compared with traditional control strategies, reinforcement learning control (RLC) exhibits more accurate and steady performances while maintaining indoor air temperature within a limited range. A case study shows that the RLC strategy is able to save substantial amounts of energy.

ACS Style

Xinfang Zhang; Zhenhai Li; Zhengwei Li; Shunian Qiu; Hai Wang. Differential pressure reset strategy based on reinforcement learning for chilled water systems. Building Simulation 2021, 1 -16.

AMA Style

Xinfang Zhang, Zhenhai Li, Zhengwei Li, Shunian Qiu, Hai Wang. Differential pressure reset strategy based on reinforcement learning for chilled water systems. Building Simulation. 2021; ():1-16.

Chicago/Turabian Style

Xinfang Zhang; Zhenhai Li; Zhengwei Li; Shunian Qiu; Hai Wang. 2021. "Differential pressure reset strategy based on reinforcement learning for chilled water systems." Building Simulation , no. : 1-16.

Journal article
Published: 26 August 2020 in Energy and Buildings
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Free cooling is commonly used in data centers to reduce cooling system energy consumption using a water-side economizer (WSE). In this system, cooling mode is switched when wet bulb temperature (Twb) reaches switchover temperature (Tsw), and the cooling tower fans are controlled to maintain a specified cooling water approach temperature (Tapp). Typically, both Tsw and Tapp values are fixed. However, under part load conditions, Tsw and Tapp can be optimized to maximize the system performance and save energy. Currently, there are few appropriate methods available to optimize Tsw and Tapp in partial-free cooling mode for data centers with WSE. Thus, this study presents a model based methodology to optimize Tsw and Tapp in different cooling modes. The proposed methodology is verified through a case study based on data of a real operating data center. Comparison between the calculated optimal Tsw and Tapp with that derived by simulation demonstrates the satisfactory accuracies of the proposed method. The case study illustrates that when Tsw and Tapp are optimized, the cooling system energy consumption can be saved by 10% when cooling load ratio is 0.6.

ACS Style

Jiajie Li; Zhengwei Li. Model-based optimization of free cooling switchover temperature and cooling tower approach temperature for data center cooling system with water-side economizer. Energy and Buildings 2020, 227, 110407 .

AMA Style

Jiajie Li, Zhengwei Li. Model-based optimization of free cooling switchover temperature and cooling tower approach temperature for data center cooling system with water-side economizer. Energy and Buildings. 2020; 227 ():110407.

Chicago/Turabian Style

Jiajie Li; Zhengwei Li. 2020. "Model-based optimization of free cooling switchover temperature and cooling tower approach temperature for data center cooling system with water-side economizer." Energy and Buildings 227, no. : 110407.

Articles
Published: 13 May 2020 in Science and Technology for the Built Environment
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Chillers consume considerable energy in building HVAC systems, and the optimal operation of chillers is essential for energy conservation in buildings. This paper proposes a model-free optimal chiller loading (OCL) method for optimizing chiller operation. Unlike model-based OCL methods, the proposed method does not require accurate chiller performance models as a priori knowledge. The proposed method is based on the Q-learning method, a classical reinforcement learning method. With the comprehensive coefficient of performance (COP) of chillers as the environmental feedback, the model-free loading controller can learn autonomously and optimize the chiller loading by adjusting the set points of the chilled water outlet temperature. A central chiller plant in an office building located in Shanghai is selected as a case system to investigate the energy conservation performance of the proposed method through simulations. The simulation results suggest that the proposed method can save 4.36% chiller energy during the first cooling season compared to the baseline control, which is slightly inferior to the value of the model-based loading method (4.95%). Owing to its acceptable energy saving capability, the proposed method can be applied to central chiller plants that lack system model and historical data.

ACS Style

Shunian Qiu; Zhenhai Li; Zhengwei Li; Xinfang Zhang. Model-free optimal chiller loading method based on Q-learning. Science and Technology for the Built Environment 2020, 26, 1100 -1116.

AMA Style

Shunian Qiu, Zhenhai Li, Zhengwei Li, Xinfang Zhang. Model-free optimal chiller loading method based on Q-learning. Science and Technology for the Built Environment. 2020; 26 (8):1100-1116.

Chicago/Turabian Style

Shunian Qiu; Zhenhai Li; Zhengwei Li; Xinfang Zhang. 2020. "Model-free optimal chiller loading method based on Q-learning." Science and Technology for the Built Environment 26, no. 8: 1100-1116.

Journal article
Published: 11 April 2020 in Energy and Buildings
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In the domain of optimal control for building HVAC systems, the performance of model-based control has been widely investigated and validated. However, the performance of model-based control highly depends on an accurate system performance model and sufficient sensors, which are difficult to obtain for certain buildings. To tackle this problem, a model-free optimal control method based on reinforcement learning is proposed to control the building cooling water system. In the proposed method, the wet bulb temperature and system cooling load are taken as the states, the frequencies of fans and pumps are the actions, and the reward is the system COP (i.e., the comprehensive COP of chillers, cooling water pumps, and cooling towers). The proposed method is based on Q-learning. Validated with the measured data from a real central chilled water system, a three-month measured data-based simulation is conducted under the supervision of four types of controllers: basic controller, local feedback controller, model-based controller, and the proposed model-free controller. Compared with the basic controller, the model-free controller can conserve 11% of the system energy in the first applied cooling season, which is greater than that of the local feedback controller (7%) but less than that of the model-based controller (14%). Moreover, the energy saving rate of the model-free controller could reach 12% in the second applied cooling season, after which the energy saving rate gets stabilized. Although the energy conservation performance of the model-free controller is inferior to that of the model-based controller, the model-free controller requires less a priori knowledge and sensors, which makes it promising for application in buildings for which the lack of accurate system performance models or sensors is an obstacle. Moreover, the results suggest that for a central chilled water system with a designed peak cooling load close to 2000 kW, three months of learning during the cooling season is sufficient to develop a good model-free controller with an acceptable performance.

ACS Style

Shunian Qiu; Zhenhai Li; Zhengwei Li; Jiajie Li; Shengping Long; Xiaoping Li. Model-free control method based on reinforcement learning for building cooling water systems: Validation by measured data-based simulation. Energy and Buildings 2020, 218, 110055 .

AMA Style

Shunian Qiu, Zhenhai Li, Zhengwei Li, Jiajie Li, Shengping Long, Xiaoping Li. Model-free control method based on reinforcement learning for building cooling water systems: Validation by measured data-based simulation. Energy and Buildings. 2020; 218 ():110055.

Chicago/Turabian Style

Shunian Qiu; Zhenhai Li; Zhengwei Li; Jiajie Li; Shengping Long; Xiaoping Li. 2020. "Model-free control method based on reinforcement learning for building cooling water systems: Validation by measured data-based simulation." Energy and Buildings 218, no. : 110055.

Conference paper
Published: 20 March 2020 in Soil and Recycling Management in the Anthropocene Era
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Data centers are specific facilities gathering quantities of servers consuming large energy for which cooling part account two-thirds. Water-side economizer (WSE) system is an effective way to reduce the cooling part consumption. In this study, an ambient wet-bulb temperature \(\left( {T_{\text{wet}} } \right)\) and approach temperature \(\left( {T_{\text{ap}} } \right)\) control strategy for WSE system is simulated in five cities in China. The result shows that \(T_{\text{ap}}\) affects the free-cooling hours and the performance characteristic of chiller and cooling tower. Under most circumstance, the optimal \(T_{\text{ap}}\) is mainly determined by chiller-cooling tower system performance characteristic while the free-cooling hours are not the dominant factor. However, when the city-ambient \(T_{\text{wet}}\) decrease, the free-cooling hours is more vital. When city-ambient \(T_{\text{wet}}\) is low enough, the energy-saving rate is higher when \(T_{\text{ap}}\) decrease because of more free-cooling hours.

ACS Style

Jiajie Li; Zhengwei Li; Hai Wang. A Study of Wet-Bulb Temperature and Approach Temperature Based Control Strategy of Water-Side Economizer Free-Cooling System for Data Center. Soil and Recycling Management in the Anthropocene Era 2020, 405 -413.

AMA Style

Jiajie Li, Zhengwei Li, Hai Wang. A Study of Wet-Bulb Temperature and Approach Temperature Based Control Strategy of Water-Side Economizer Free-Cooling System for Data Center. Soil and Recycling Management in the Anthropocene Era. 2020; ():405-413.

Chicago/Turabian Style

Jiajie Li; Zhengwei Li; Hai Wang. 2020. "A Study of Wet-Bulb Temperature and Approach Temperature Based Control Strategy of Water-Side Economizer Free-Cooling System for Data Center." Soil and Recycling Management in the Anthropocene Era , no. : 405-413.

Journal article
Published: 04 May 2019 in Energy and Buildings
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Chillers are responsible for nearly 30–40% of the energy consumption of an HVAC system. To enhance the efficiency of HVAC systems, it is necessary to optimize the operation of chillers. Conventional chiller operation strategies are mostly intended to save energy by suitably distributing the chiller cooling load in accordance with the measured cooling system data, which is typically considered noise-free. In other words, the uncertainty of the cooling system measurement and the necessity of customized optimization—as different buildings require different tradeoffs between energy-saving and indoor comfort—are usually not considered. This paper proposes a stochastic optimized chiller operation strategy based on multi-objective optimization and measurement uncertainty. The strategy consists of five steps: (1) Quantify the indoor comfort utility and energy consumption utility by using utility functions; (2) integrate these two utilities into one comprehensive utility using user-defined weights; (3) specify the measurement uncertainty distribution in the cooling system; (4) traverse each possible operation plan, and calculate the mean value (i.e., mathematical expectation) of its corresponding comprehensive utility (expected utility, EU); and (5) determine the optimal operation plan to be the one corresponding to the maximum EU value. The performance of the proposed strategy is validated by establishing a cooling system model on TRNSYS and comparing the findings with those of two conventional chiller operation strategies commonly adopted in Shanghai: the cooling load control strategy and coefficient of performance (COP) optimization strategy. Moreover, the robustness of the proposed strategy is validated by performing a comparison with the deterministic multi-objective strategy, which assumes that the measured system data is noise-free. The simulation results suggest that the proposed strategy can be customized to satisfy different optimization requirements such as less energy consumption or a high level of indoor comfort.

ACS Style

Shunian Qiu; Fan Feng; Weijie Zhang; Zhengwei Li; Zhenhai Li. Stochastic optimized chiller operation strategy based on multi-objective optimization considering measurement uncertainty. Energy and Buildings 2019, 195, 149 -160.

AMA Style

Shunian Qiu, Fan Feng, Weijie Zhang, Zhengwei Li, Zhenhai Li. Stochastic optimized chiller operation strategy based on multi-objective optimization considering measurement uncertainty. Energy and Buildings. 2019; 195 ():149-160.

Chicago/Turabian Style

Shunian Qiu; Fan Feng; Weijie Zhang; Zhengwei Li; Zhenhai Li. 2019. "Stochastic optimized chiller operation strategy based on multi-objective optimization considering measurement uncertainty." Energy and Buildings 195, no. : 149-160.

Conference paper
Published: 04 March 2019 in IOP Conference Series: Earth and Environmental Science
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Deterministic chiller optimization control strategies, such as COP optimization strategy, are intended to save energy based on deterministic sensor measuring data and equipment characteristics. However, the sensor data and the equipment characteristics are typically uncertain due to poor calibration of sensors, poor maintenance of chillers, etc., which could harm the energy-saving performance of deterministic chiller optimization operation strategies. In order to tackle this problem, a stochastic chiller optimization operation strategy based on uncertainty analysis is proposed in this paper. The strategy consists of three steps: (1) Analyze the uncertainty of the HVAC system and specify the probability distribution of each uncertain parameter. (2) Calculate the mathematical expectation value of energy consumption and return chilled water temperature in each operation plan under uncertainty. (3) Select the operation plan with the least energy consumption expectation and limited return chilled water temperature. The performance of the proposed strategy is validated on TRNSYS with measured hourly cooling load data of an office building located in Shanghai. Compared with the deterministic optimized operation strategy, the proposed stochastic strategy performs better on robustness (i.e., keeping return chilled water temperature within safe criteria) because of the consideration of measurement uncertainty. Also, compared with traditional operation strategy without optimization, the proposed strategy performs better on saving energy

ACS Style

Shunian Qiu; Weijie Zhang; Fan Feng; Zhengwei Li; Zhenhai Li. A stochastic chiller optimization operation strategy based on uncertainty analysis. IOP Conference Series: Earth and Environmental Science 2019, 238, 012023 .

AMA Style

Shunian Qiu, Weijie Zhang, Fan Feng, Zhengwei Li, Zhenhai Li. A stochastic chiller optimization operation strategy based on uncertainty analysis. IOP Conference Series: Earth and Environmental Science. 2019; 238 (1):012023.

Chicago/Turabian Style

Shunian Qiu; Weijie Zhang; Fan Feng; Zhengwei Li; Zhenhai Li. 2019. "A stochastic chiller optimization operation strategy based on uncertainty analysis." IOP Conference Series: Earth and Environmental Science 238, no. 1: 012023.

Journal article
Published: 01 October 2018 in Energy Procedia
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30%-40% energy consumption of the HVAC system is caused by chillers. The energy consumption of chillers highly depends on the quality of the operation strategy. A chiller operation strategy based on multiple-objective optimization is proposed in this paper. The strategy consists of three steps: (1) With effectiveness functions, two indicators of the operation goodness, indoor comfort and energy consumption, are quantified to two indicators (2) These two indicators are integrated to one comprehensive indicator. (3) The optimal operation condition is determined by an optimization algorithm to maximize the comprehensive indicator. The parameters in this strategy is defined according to on-site surveys, to improve the application value of the strategy. The effectiveness of the proposed strategy is validated on TRNSYS compared with the 3 common operation strategies adopted in buildings of Shanghai. The simulation results suggest that the proposed strategy is able to save energy of HVAC system with limited loss of indoor comfort. Besides, due to the appropriate arrangement of operation order, the proposed strategy could balance the working time of each chiller to put off the discard of chillers.

ACS Style

Shunian Qiu; Weijie Zhang; Jiajie Li; Jialiang Chen; Zhenhai Li; Zhengwei Li. A chiller operation strategy based on multiple-objective optimization. Energy Procedia 2018, 152, 318 -323.

AMA Style

Shunian Qiu, Weijie Zhang, Jiajie Li, Jialiang Chen, Zhenhai Li, Zhengwei Li. A chiller operation strategy based on multiple-objective optimization. Energy Procedia. 2018; 152 ():318-323.

Chicago/Turabian Style

Shunian Qiu; Weijie Zhang; Jiajie Li; Jialiang Chen; Zhenhai Li; Zhengwei Li. 2018. "A chiller operation strategy based on multiple-objective optimization." Energy Procedia 152, no. : 318-323.

Journal article
Published: 01 October 2018 in Energy Procedia
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The utilization of distributed power generation system plays an essential role in the design of resilient cities. With the ever increasing of efficiency and decreasing of production cost, Photovoltaic (PV) system is becoming a globally welcomed city component. During the design of PV systems, the matching of demand and supply power curve is critical. This paper evaluates the PV usage potential in different neighborhoods of Shanghai through matching the rooftop PV generation profile with the electricity demands of office buildings. The typical electricity use pattern of an office building is calculated by clustering method with data from a sub-metering platform, and PV generation curve is generated through simulation approach. Case studies were done in 140 neighborhoods of Shanghai City, and theoretical self-utilization ratio and PV utilization of each neighborhood were calculated.

ACS Style

Jialiang Chen; Xin Wang; Zhengwei Li; Shunian Qiu; Jiang Wu. Deploying residential rooftop PV units for office building use: a case study in Shanghai. Energy Procedia 2018, 152, 21 -26.

AMA Style

Jialiang Chen, Xin Wang, Zhengwei Li, Shunian Qiu, Jiang Wu. Deploying residential rooftop PV units for office building use: a case study in Shanghai. Energy Procedia. 2018; 152 ():21-26.

Chicago/Turabian Style

Jialiang Chen; Xin Wang; Zhengwei Li; Shunian Qiu; Jiang Wu. 2018. "Deploying residential rooftop PV units for office building use: a case study in Shanghai." Energy Procedia 152, no. : 21-26.

Research article
Published: 27 September 2018 in Building Simulation
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Operation strategies influence the building energy efficiency. In order to enhance the building energy efficiency, it’s necessary to adopt proper operation strategies on building equipment. Thus, the identification of existing operation strategies is necessary for the improvement of operation strategies. A data mining (DM) based framework is proposed in this paper to automatically identify the building operation strategies. The framework includes classification and regression tree (CART), and weighted association rule mining (WARM) method, targeting at three types of rule based control strategies: on/off control, sequencing control (for equipment of the same type), and coordinated control (for equipment of different types). The performance of this framework is validated with power metering system data and manual identification results based on on-site survey of three buildings in Shanghai. The validation results suggest that the proposed framework is capable of identifying building operation strategies accurately and automatically. Implemented on the original software named BOSA (Building Operation Strategy Analysis), this framework is promising to be used in engineering field to enhance the efficiency of building operation strategy identification work.

ACS Style

Shunian Qiu; Fan Feng; Zhengwei Li; Guang Yang; Peng Xu; Zhenhai Li. Data mining based framework to identify rule based operation strategies for buildings with power metering system. Building Simulation 2018, 12, 195 -205.

AMA Style

Shunian Qiu, Fan Feng, Zhengwei Li, Guang Yang, Peng Xu, Zhenhai Li. Data mining based framework to identify rule based operation strategies for buildings with power metering system. Building Simulation. 2018; 12 (2):195-205.

Chicago/Turabian Style

Shunian Qiu; Fan Feng; Zhengwei Li; Guang Yang; Peng Xu; Zhenhai Li. 2018. "Data mining based framework to identify rule based operation strategies for buildings with power metering system." Building Simulation 12, no. 2: 195-205.

Journal article
Published: 01 August 2018 in Energy and Buildings
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When modeling the energy performance of existing buildings, model calibration always serves as an essential and necessary step to ensure the accuracy and applicability of building models. Model calibration for detailed energy models, which is also known as calibrated simulation (CS), refers to the process of tuning model's input parameters to narrow down the mismatch between the simulation result and the real-monitored data of building energy consumptions. Two major problems of current CS are:(1) a successful calibration requires HVAC domain knowledge of modelers. (2) Traditional building calibration process is labor-intensive and time-consuming. To solve these problems, a normative energy modeling based quick auto-calibration approach is proposed in this study. This approach is able to reduce the modeling time and simplify the calibration procedures. Firstly, this paper demonstrates the working principles and the credibility of Normative Energy Modeling (NEM). Then, the methodology of the proposed quick auto-calibration approach is elaborated. The energy performance model of a large hotel located in Shanghai, China with NEM is used as a case study to investigate the effectiveness of the proposed quick auto-calibration approach. The simulation result of the case study suggests that the proposed approach can significantly simplify the procedures of model calibrations while still achieve a good accuracy specified by ASHRAE Guideline 14 [1]. Besides, the results further confirm that such method requires much less time and computational power for modeling, compared with other counterparts (e.g., “Autotune” calibration [2]). With the advantage of speed and accuracy, the auto-calibration proposed in this paper is promising to be applied in both engineering and research field.

ACS Style

Shunian Qiu; Zhengwei Li; Zhihong Pang; Weijie Zhang; Zhenhai Li. A quick auto-calibration approach based on normative energy models. Energy and Buildings 2018, 172, 35 -46.

AMA Style

Shunian Qiu, Zhengwei Li, Zhihong Pang, Weijie Zhang, Zhenhai Li. A quick auto-calibration approach based on normative energy models. Energy and Buildings. 2018; 172 ():35-46.

Chicago/Turabian Style

Shunian Qiu; Zhengwei Li; Zhihong Pang; Weijie Zhang; Zhenhai Li. 2018. "A quick auto-calibration approach based on normative energy models." Energy and Buildings 172, no. : 35-46.

Journal article
Published: 12 June 2017 in Science and Technology for the Built Environment
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ACS Style

Guang Yang; Zhengwei Li; Godfried Augenbroe. Development of prototypical buildings for urban scale building energy modeling: A reduced order energy model approach. Science and Technology for the Built Environment 2017, 24, 33 -42.

AMA Style

Guang Yang, Zhengwei Li, Godfried Augenbroe. Development of prototypical buildings for urban scale building energy modeling: A reduced order energy model approach. Science and Technology for the Built Environment. 2017; 24 (1):33-42.

Chicago/Turabian Style

Guang Yang; Zhengwei Li; Godfried Augenbroe. 2017. "Development of prototypical buildings for urban scale building energy modeling: A reduced order energy model approach." Science and Technology for the Built Environment 24, no. 1: 33-42.

Journal article
Published: 01 May 2017 in Energy Procedia
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ACS Style

Xin Wang; Zhengwei Li. A Systematic Approach to Evaluate the Impact of Urban form on Urban Energy Efficiency: A Case Study in Shanghai. Energy Procedia 2017, 105, 3225 -3231.

AMA Style

Xin Wang, Zhengwei Li. A Systematic Approach to Evaluate the Impact of Urban form on Urban Energy Efficiency: A Case Study in Shanghai. Energy Procedia. 2017; 105 ():3225-3231.

Chicago/Turabian Style

Xin Wang; Zhengwei Li. 2017. "A Systematic Approach to Evaluate the Impact of Urban form on Urban Energy Efficiency: A Case Study in Shanghai." Energy Procedia 105, no. : 3225-3231.

Journal article
Published: 01 May 2017 in Energy Procedia
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ACS Style

Fan Feng; Zhengwei Li. A Methodology to Identify Multiple Equipment Coordinated Control with Power Metering System. Energy Procedia 2017, 105, 2499 -2505.

AMA Style

Fan Feng, Zhengwei Li. A Methodology to Identify Multiple Equipment Coordinated Control with Power Metering System. Energy Procedia. 2017; 105 ():2499-2505.

Chicago/Turabian Style

Fan Feng; Zhengwei Li. 2017. "A Methodology to Identify Multiple Equipment Coordinated Control with Power Metering System." Energy Procedia 105, no. : 2499-2505.

Journal article
Published: 01 March 2017 in Energy and Buildings
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ACS Style

Yingjun Ruan; Jiahui Cao; Fan Feng; Zhengwei Li. The role of occupant behavior in low carbon oriented residential community planning: A case study in Qingdao. Energy and Buildings 2017, 139, 385 -394.

AMA Style

Yingjun Ruan, Jiahui Cao, Fan Feng, Zhengwei Li. The role of occupant behavior in low carbon oriented residential community planning: A case study in Qingdao. Energy and Buildings. 2017; 139 ():385-394.

Chicago/Turabian Style

Yingjun Ruan; Jiahui Cao; Fan Feng; Zhengwei Li. 2017. "The role of occupant behavior in low carbon oriented residential community planning: A case study in Qingdao." Energy and Buildings 139, no. : 385-394.

Journal article
Published: 01 December 2016 in Energy Procedia
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ACS Style

Li Yuan; Yingjun Ruan; Guang Yang; Fan Feng; Zhengwei Li. Analysis of Factors Influencing the Energy Consumption of Government Office Buildings in Qingdao. Energy Procedia 2016, 104, 263 -268.

AMA Style

Li Yuan, Yingjun Ruan, Guang Yang, Fan Feng, Zhengwei Li. Analysis of Factors Influencing the Energy Consumption of Government Office Buildings in Qingdao. Energy Procedia. 2016; 104 ():263-268.

Chicago/Turabian Style

Li Yuan; Yingjun Ruan; Guang Yang; Fan Feng; Zhengwei Li. 2016. "Analysis of Factors Influencing the Energy Consumption of Government Office Buildings in Qingdao." Energy Procedia 104, no. : 263-268.

Journal article
Published: 01 December 2016 in Energy Procedia
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ACS Style

Fan Feng; Zhengwei Li; Yingjun Ruan; Peng Xu. An Empirical Study of Influencing Factors on Residential Building Energy Consumption in Qingdao City, China. Energy Procedia 2016, 104, 245 -250.

AMA Style

Fan Feng, Zhengwei Li, Yingjun Ruan, Peng Xu. An Empirical Study of Influencing Factors on Residential Building Energy Consumption in Qingdao City, China. Energy Procedia. 2016; 104 ():245-250.

Chicago/Turabian Style

Fan Feng; Zhengwei Li; Yingjun Ruan; Peng Xu. 2016. "An Empirical Study of Influencing Factors on Residential Building Energy Consumption in Qingdao City, China." Energy Procedia 104, no. : 245-250.

Journal article
Published: 31 May 2016 in Science and Technology for the Built Environment
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ACS Style

Yangyang Fu; Zhengwei Li; Fan Feng; Peng Xu. Data-quality detection and recovery for building energy management and control systems (EMCSs): Case study on submetering. Science and Technology for the Built Environment 2016, 22, 798 -809.

AMA Style

Yangyang Fu, Zhengwei Li, Fan Feng, Peng Xu. Data-quality detection and recovery for building energy management and control systems (EMCSs): Case study on submetering. Science and Technology for the Built Environment. 2016; 22 (6):798-809.

Chicago/Turabian Style

Yangyang Fu; Zhengwei Li; Fan Feng; Peng Xu. 2016. "Data-quality detection and recovery for building energy management and control systems (EMCSs): Case study on submetering." Science and Technology for the Built Environment 22, no. 6: 798-809.

Journal article
Published: 01 October 2015 in Applied Energy
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ACS Style

Liang Zhang; Peng Xu; Jiachen Mao; Xu Tang; Zhengwei Li; Jianguo Shi. A low cost seasonal solar soil heat storage system for greenhouse heating: Design and pilot study. Applied Energy 2015, 156, 213 -222.

AMA Style

Liang Zhang, Peng Xu, Jiachen Mao, Xu Tang, Zhengwei Li, Jianguo Shi. A low cost seasonal solar soil heat storage system for greenhouse heating: Design and pilot study. Applied Energy. 2015; 156 ():213-222.

Chicago/Turabian Style

Liang Zhang; Peng Xu; Jiachen Mao; Xu Tang; Zhengwei Li; Jianguo Shi. 2015. "A low cost seasonal solar soil heat storage system for greenhouse heating: Design and pilot study." Applied Energy 156, no. : 213-222.