Verifying of the feasibility and energy efficiency of the largest certified passive house office building in China: A three-year performance monitoring study

Han, Fei; Liu, Bin; Wang, Yang; Dermentzis, Georgios; Cao, Xinyun; Zhao, Lingmin; Pfluger, Rainer; Feist, Wolfgang

doi:10.1016/j.jobe.2021.103703

Cited by 7 publications

(9 citation statements)

References 26 publications

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“…The Passive House Technique and Experience Centre (PHTEC) building, 47 is a PHI-certified building located in the Sino-German Ecopark in Qingdao, China (Figure 2). Qingdao is located at 36°4 0 N, 120°19 0 E, on the coast of the Yellow Sea.…”

Section: Building Descriptionmentioning

confidence: 99%

Investigation of the indoor environment of a passive house office building under cold climate in China

Han

Zhao

et al. 2022

Indoor and Built Environment

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This study analyzed the applicability of the Passive House standard to office buildings in northern China from an indoor environment perspective. The case building has been auto-monitored continuously since 2017 and a survey for instantaneous and retrospective appraisal of indoor environmental conditions was administered in 2021 during the summer, winter and months in between, referred to as ‘transition months’. The results show that indoor environment quality was good, with high satisfaction levels and ultra-low energy consumption; overheating and the need for window opening were observed. The prevalence of sick building syndrome was reduced compared to conventional buildings. This work further determined how comfort parameters such as perceived thermal comfort, indoor air quality and acoustics could affect end-users’ appraisal. Findings show that indoor thermal comfort design parameters, particularly in summer, are critical factors affecting users' satisfaction in Passive Houses. We further examined the feasibility of the PMV-PPD index for predicting occupants’ perceived comfort and suggested deriving the preferred thermal comfort design parameters for the Chinese market, using long-term data and measurements. These findings provide insights into the localization of Passive Houses in China, thereby improving the acceptance of Passive Houses in what is expected to be a fast growing market.

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Section: Building Descriptionmentioning

confidence: 99%

Investigation of the indoor environment of a passive house office building under cold climate in China

Han

Zhao

et al. 2022

Indoor and Built Environment

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“…The monitoring system of Testbed EM logs indoor temperature, CO 2 concentration, electricity use and domestic hot water use in all 305 apartments with a sample rate of 1 min. An indicator, called Missed Data Points Ratio (MDPR), was defined as the available number of measurements over the expected number of measurements based on the sample rate of 1 min to quantify the data availability over the 12-month period of analysis (Equation (1)). An MDPR of 100% would indicate that all measurements are available as expected.…”

Section: Data Quantitymentioning

confidence: 99%

“…Smart buildings typically deploy, among others, more advanced sensor capabilities and advanced monitoring systems. Data from monitoring systems has for instance been used to monitor the energy performance of buildings during operation [1][2][3], to highlight significant energy performance gaps and to identify the reasons for these discrepancies between expected energy use from design and actual monitored energy use, [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Evaluation of Comfort, Indoor Air Quality and Energy Performance in Buildings: The Case of the KTH Live-In Lab Testbeds

2022

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Digitalization offers new, unprecedented possibilities to increase the energy efficiency and improve the indoor conditions in buildings in a cost-efficient way. Smart buildings are seen by many stakeholders as the way forward. Smart buildings feature advanced monitoring and control systems that allow a better control of the buildings’ indoor spaces, but it is becoming evident that the massive amount of data produced in smart buildings is rarely used. This work presents a long-term evaluation of a smart building testbed for one year; the building features state-of-the-art monitoring capability and local energy generation (PV). The analysis shows room for improving energy efficiency and indoor comfort due to non-optimal control settings; for instance, average indoor temperatures in all winter months were above 24 °C. The analysis of electricity and domestic hot water use has shown a relevant spread in average use, with single users consuming approximately four times more than the average users. The combination of CO2 and temperature sensor was sufficient to pinpoint the anomalous operation of windows in wintertime, which has an impact on energy use for space heating. Although the quantification of the impact of users on the overall energy performance of the building was beyond the scope of this paper, this study showcases that modern commercial monitoring systems for buildings have the potential to identify anomalies. The evidence collected in the paper suggests that this data could be used to promote energy-efficient behaviors among building occupants and shows that cost-effective actions could be carried out if data generated by the monitoring and control systems were used more extensively.

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“…The impact of occupancy on the building cooling load is mostly related to the use of appliances. Since the room is designed for a specific number of people and appropriate activities to be performed within it, overheating can sometimes occur when we move away from the designed specifications [6][7][8][9], and affect the indoor comfort. The cooling within the building can be improved by the choice of material with excellent thermal inertia [6] that will impact on the radiant time series factors of the cooling load model of the building.…”

Section: Introductionmentioning

confidence: 99%

“…Since the room is designed for a specific number of people and appropriate activities to be performed within it, overheating can sometimes occur when we move away from the designed specifications [6][7][8][9], and affect the indoor comfort. The cooling within the building can be improved by the choice of material with excellent thermal inertia [6] that will impact on the radiant time series factors of the cooling load model of the building. Avoiding overcooling and maintaining the best thermal comfort [10] are the main goals of the heating, ventilation, and air conditioning (HVAC) system in the building.…”

Section: Introductionmentioning

confidence: 99%

Development of a Linear Regression Model Based on the Most Influential Predictors for a Research Office Cooling Load

Mutombo

Numbi

2022

Energies

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Energy consumption in the building sector is a major concern, particularly in this time of worldwide population and energy demand increases. To reduce energy consumption due to HVAC systems in the building sector, different models based on measured data have been developed to estimate the cooling load. The purpose of this work is to develop a linear regression model for cooling load of a research room based on the radiant time series (RTS) components of the cooling load that consider the building material and the environment. Using the forward step method, linear regression models were developed for both all-seasons and seasonal data from three years of cooling load data obtained from the RTS method for a research room at Mangosuthu University of Technology (MUT), South Africa. The male and female occupants, window cooling load, and roof cooling load were found to be the most influential predictors for the cooling load model. The obtained relative errors between the best all-seasons model and seasonal models built with the same predictors for the respective data subsets are almost zero and are given as 0.0073% (autumn), 0.0016% (spring), 0.0168% (summer), and 0.0162% (winter). This leads to the conclusion that the seasonal models can be represented by the all-seasons model. However, further study can be performed to improve the model by incorporating the occupancy behaviours and other components or parameters intervening in the calculation of cooling load using the radiant time series method.

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Verifying of the feasibility and energy efficiency of the largest certified passive house office building in China: A three-year performance monitoring study

Cited by 7 publications

References 26 publications

Investigation of the indoor environment of a passive house office building under cold climate in China

Investigation of the indoor environment of a passive house office building under cold climate in China

Long-Term Evaluation of Comfort, Indoor Air Quality and Energy Performance in Buildings: The Case of the KTH Live-In Lab Testbeds

Development of a Linear Regression Model Based on the Most Influential Predictors for a Research Office Cooling Load

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