Optimization of cool roof and night ventilation in office buildings: A case study in Xiamen, China

Increasing roof albedo (using a “cool” roof) and night ventilation are passive cooling technologies that can reduce the cooling loads in buildings, but existing studies have not comprehensively explored the potential benefits of integrating these two technologies. This study combines an experiment in the summer and transition seasons with an annual simulation so as to evaluate the thermal performance, energy savings and thermal comfort improvement that could be obtained by coupling a cool roof with night ventilation. A holistic approach integrating sensitivity analysis and multi-objective optimization is developed to explore key design parameters (roof albedo, night ventilation air change rate, roof insulation level and internal thermal mass level) and optimal design options for the combined application of the cool roof and night ventilation. The proposed approach is validated and demonstrated through studies on a six-storey office building in Xiamen, a cooling-dominated city in southeast China. Simulations show that combining a cool roof with night ventilation can significantly decrease the annual cooling energy consumption by 27% compared to using a black roof without night ventilation and by 13% compared to using a cool roof without night ventilation. Roof albedo is the most influential parameter for both building energy performance and indoor thermal comfort. Optimal use of the cool roof and night ventilation can reduce the annual cooling energy use by 28% during occupied hours when air-conditioners are on and reduce the uncomfortable time slightly during occupied hours when air-conditioners are off

System-level key performance indicators for building performance evaluation

Quantifying building energy performance through the development and use of key performance indicators (KPIs) is an essential step in achieving energy saving goals in both new and existing buildings. Current methods used to evaluate improvements, however, are not well represented at the system-level (e.g., lighting, plug-loads, HVAC, service water heating). Instead, they are typically only either measured at the whole building level (e.g., energy use intensity) or at the equipment level (e.g., chiller efficiency coefficient of performance (COP)) with limited insights for benchmarking and diagnosing deviations in performance of aggregated equipment that delivers a specific service to a building (e.g., space heating, lighting). The increasing installation of sensors and meters in buildings makes the evaluation of building performance at the system level more feasible through improved data collection. Leveraging this opportunity, this study introduces a set of system-level KPIs, which cover four major end-use systems in buildings: lighting, MELs (Miscellaneous Electric Loads, aka plug loads), HVAC (heating, ventilation, and air-conditioning), and SWH (service water heating), and their eleven subsystems. The system KPIs are formulated in a new context to represent various types of performance, including energy use, peak demand, load shape, occupant thermal comfort and visual comfort, ventilation, and water use. This paper also presents a database of system KPIs using the EnergyPlus simulation results of 16 USDOE prototype commercial building models across four vintages and five climate zones. These system KPIs, although originally developed for office buildings, can be applied to other building types with some adjustment or extension. Potential applications of system KPIs for system performance benchmarking and diagnostics, code compliance, and measurement and verification are discussed

Overview of methods to analyse dynamic data

This book gives an overview of existing data analysis methods to analyse the dynamic data obtained from full scale testing, with their advantages and drawbacks. The overview of full scale testing and dynamic data analysis is limited to energy performance characterization of either building components or whole buildings. The methods range from averaging and regression methods to dynamic approaches based on system identification techniques. These methods are discussed in relation to their application in following in situ measurements: -measurement of thermal transmittance of building components based on heat flux meters; -measurement of thermal and solar transmittance of building components tested in outdoor calorimetric test cells; -measurement of heat transfer coefficient and solar aperture of whole buildings based on co-heating or transient heating tests; -characterisation of the energy performance of whole buildings based on energy use monitoring

Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4 °C for the cooling mode with a median of 23.7 °C, and between 20.5 and 24.9 °C for the heating mode with a median of 22.7 °C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2–3 °C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking

Mitigating energy poverty: Potential contributions of combining PV and building thermal mass storage in low-income households

The issue of energy poverty has devastating implications for the society, and it has been aggravated in the past years due to the economic crisis and the increase of energy prices. Among the most affected are those with low incomes and living in inefficient buildings. Unfortunately, the bitter reality is that sometimes this part of the population are facing the next question: Heating, or eating? The declining prices of distributed energy technologies such as photovoltaics provides an opportunity for positive social change. Although their use does not address energy poverty directly, substantial contributions may be made. Measurements of indoor temperatures in a social housing district of southern Spain in 2017 have revealed the unbearable temperatures that the occupants have to endure, both in summer and winter. Using this district as a case study, the present work aims to evaluate the benefits of exploiting its rooftop PV potential to cover part of the electricity consumption of the district (reducing the energy bills), and use the surplus electricity to supply power for the heat pumps in the district. Optimal alternatives regarding maximum PV production, maximum self-sufficiency ratio and minimum investment costs have been found, considering as well different options when sharing the available electricity surplus to improve the thermal comfort of the occupants. As far as the authors know, no previous study has followed an approach aimed at energy poverty alleviation such as the one presented in this work. The results show that using the surplus electricity to heat or cool the whole dwellings would improve the thermal comfort of the occupants in average up to 11% in winter and 26% in summer. If all the PV generation was used or more buildings in the area were employed to install PV modules, improvements up to 33% in winter and 67% in summer could be obtained, reducing at the same time the thermal comfort differences among the dwellings of the district

Green buildings and design for adaptation: strategies for renovation of the built environment

The recent EU Directives 2010/31 and 2012/27 provide standards of nearly zero energy buildings for new constructions, aiming at a better quality of the built environment through the adoption of high-performance solutions. In the near future, cities are expected to be the main engine of development while bearing the impact of population growth: new challenges such as increasing energy efficiency, reducing maintenance costs of buildings and infrastructures, facing the effects of climate change and adjusting on-going and future impacts, require smart and sustainable approaches. To improve the capability of adaptation to dynamics of transformation, buildings and districts have to increase their resilience, assumed as ‘the capacity to adapt to changing conditions and to maintain or regain functionality and vitality in the face of stress or disturbance’ (Wilson A., Building Resilience in Boston, Boston Society of Architects, 2013). This paper describes the research methodology, developed by the Department of Architecture, a research unit of Technology for Architecture, to perform the assessment of resilience of existing buildings, as well as the outcomes of its application within Bologna urban context. This methodology focuses on the design for adaptation of social housing buildings, aiming at predicting their expected main impacts (energy consumption, emissions, efficiency, urban quality and environmental sustainability) and at developing models for renovation