• Energy Research

A primary contributor to urban overheating is the urban heat island (UHI) formed due to increased urbanization. The adverse effects of UHI on building energy use are substantial and well documented. However, such effects are typically demonstrated through numerical simulations which are susceptible to modeling uncertainties and lack of validation resulting in a pressing research gap. Here, for the first time, we conduct a large-scale assessment to demonstrate the devastating impact of UHI on building energy consumption using real building energy use data. We find empirical evidence correlating UHI with building energy use; changes in average UHI intensity of 0.5 K correspond to an increase in monthly cooling energy consumption in a range of 0.17 kWh/m2-1.84 kWh/m2. The study validates theoretical evidence on the impact of UHI on building energy and proposes a highly innovative methodology to assess the impact of overheating on the energy balance of cities.

In this study, a theoretical ventilated photovoltaic (PV) facade, which functions as a pre-heating device in winter and a natural ventilation system in summer and reduces PV module temperatures, was analysed. The interrelationship between an optimum proportion of transparent window (and an opaque PV module) to the total facade area, and the variables relevant to the energy performance was assessed. The design parameters under consideration have been categorised according to climate, building characteristics, facade configurations and PV system elements. One outcome of this investigation is a new index, effectiveness of a PV Facade (PVEF), that has been developed to evaluate the overall energy performance of a PV facade with regard to the proportion of useful daylight that may displace the use of electric lighting, and the electricity generated by the PV modules to the heating and cooling energy consumption within a building. In conclusion, the electricity generation and the factors, affecting the ventilation performance of a ventilated PV facade, have been presented.

Abstract Predicting the heating and cooling (HC) energy performance of a building is essential in the understanding and energy-efficient control of HC systems. The aims of this study were to develop and propose advanced and accurate energy prediction models using deep learning techniques. Also, to assess the importance and the significance of the relevant variables used in the models. The models were developed based on measured data collected in an educational building and were classified into different prediction time groups at 3-min, 15-min, 30-min, and 1-h time intervals. The inputs used in the models for the HC system and the EHP were selected through a variable selection process based on domain knowledge and correlation analysis. The results also indicate that the operational factors of the HC system had greater influence on the energy consumption than the indoor and outdoor temperatures. The performances of developed models indicate that a deep learning approach can be effectively applied to predict and understand the electric energy consumption of a HC system. Furthermore, the variable selection process and the important variables identified through it can be applied to energy prediction of HC systems in other buildings.

Abstract Clothing insulation is a key variable in the prediction of occupant thermal comfort. Consequently, the aim of the current study was to develop predictive models that forecast clothing insulation levels of building occupants. Using field measurements, we investigated the influence of outdoor environment factors and mode of transport on clothing insulation levels of university students. Our results showed that both the mode of transport and weather variables influenced the clothing insulation levels of the students. We then developed a deep neural network model that forecasts mean daily clothing insulation levels using outdoor air temperature at 6 am, dew point temperature at 6 am, gender, season and mode of transport in the based on the collected data from 1316 questionnaire surveys. In addition, we revealed that outdoor environment factors had stronger associations with clothing insulation levels than indoor environment elements. The developed deep neural network model indicated a high R² value of 0.90. In comparison to the deep neural network model, a developed linear model using the same data indicated a lower R² value of 0.698, which implies that the proposed deep neural network model provides an efficient method to forecast clothing insulation levels.

Abstract The urban heat island is increasing the temperature of cities up to 10 °C and has a very important impact on energy, environmental quality and health. Materials used in the building and urban fabric affect the urban thermal balance and contribute highly to urban overheating. The article presents the progress achieved on the design, development and implementation of mitigation materials presenting a low and very low surface temperature. The recent technological progress and developments concerning natural, light colour, IR reflective, PCM doped, thermochromic, fluorescent, photonic and plasmonic materials is presented. Experimental results on the cooling capacity and the thermal performance of conventional and advanced materials are described in a comparative way. It is demonstrated that innovative materials can exhibit sub-ambient surface temperatures and contribute highly to mitigate urban overheating.

Climate change influences urban mortality. The magnitude of such influences differs from locality to locality and is fundamentally driven by a facet of factors that include changes in local climatic conditions, demographics, and social-economic factors. Here, we employ regression and clustering methods to study linkages between mortality and local climatic changes in Seoul. Personal factors of the deceased (e.g., age and gender), social-economic factors (i.e., education level), and outdoor climatic factors, including heatwaves (HWs) and the urban heat island (UHI) phenomenon are considered in the analysis. We find that, among many elements of outdoor weather factors considered, the apparent temperature mostly correlated to daily mortalities; the mortality risk to apparent temperature exposure is more heightened for males (RR = 0.40, 95% CI; 0.23–0.54) than females (RR = 0.05, 95% CI; −0.10–0.20) at higher apparent temperatures (i.e., 60 °C). Furthermore, the influence of HWs on mortality is more apparent in the “Male” gender group and the “Above 65” age group. The results are useful in identifying vulnerable demographics amid the changing climate, especially in urban areas, and are fundamental in developing policies that promote climate resilience and adaptation.

Abstract As global warming continues, the current trend implies that the uptake of air conditioning in the residential sector will go up, thus potentially increasing domestic cooling energy consumption. In this context, this paper investigates the significance of behavioural, physical and socio-economic parameters on cooling energy in order to improve energy efficiency in residential buildings. It demonstrates that such factors exert a significant indirect as well as direct influence on energy use, showing that it is particularly important to understand indirect relationships. An initial study of direct factors affecting cooling energy reveals that occupant behaviour is the most significant issue (related to choices about how often and where air conditioning is used). This is broadly confirmed by path analysis, although climate is seen to be the single most significant parameter, followed by behavioural issues, key physical parameters (e.g. air conditioning type), and finally socio-economic aspects (e.g. household income).

Abstract The variable refrigerant flow system has received much attention due to its energy saving potential and design flexibility. A load responsive control of the evaporating control in the VRF system, which aims to reduce the cooling energy consumption of the VRF system, has been developed. This newly developed control consists of two algorithms. It starts with the load determinant algorithm, which evaluates the levels of internal cooling loads of individual indoor units. The second algorithm determines the target evaporating temperature based on the outcome of the first algorithm. A series of experiments in a multicalorimeter, which is composed of one outdoor unit chamber and two indoor unit chambers, indicates that increasing the evaporating temperature can reduce the electricity consumption of the VRF system by up to 35% without impairing the energy efficiency of the VRF system. The annual energy consumption of a typical office building with a VRF system has been simulated with whole-building energy simulation (EnergyPlus), and the EnergyPlus runtime language is used to make a code to model the performance variation of the VRF system as a function of the evaporating temperature. Our simulation results demonstrate that the annual cooling energy consumption is lowered by 14%.