Does window-to-wall ratio have a significant effect on the energy consumption of buildings? A parametric analysis in Italian climate conditions

Marino, Concettina; Nucara, Antonino; Pietrafesa, Matilde

doi:10.1016/j.jobe.2017.08.001

Cited by 127 publications

(73 citation statements)

References 45 publications

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“…The authors concluded that for the cooling mode the energy performance of windows is influenced significantly by their thermophysical properties, and that fenestration elements with low thermal transmittance seem to have a detrimental impact in the energy performance, since it prohibits the dissipation of heat toward the ambient environment and results in higher cooling demands. Marino et al [8] performed a parametric study on the effect of the window-to-wall ratio (W W R) on the energy consumption of the Italian building stock. The authors concluded that albeit energy consumption demonstrated to be strongly influenced by climate conditions, insulation features of the structures, facade configurations, presence of shading devices, the optimal W W R does not seem to vary remarkably if the effect of each factor is assessed individually.…”

Section: Introductionmentioning

confidence: 99%

The impact of thermal transmittance variation on building design in the Mediterranean region

et al. 2019

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Contrarily to what happens in northern European countries, buildings in the Mediterranean region are prone to overheating. Consequently, it is important to better understand the role of thermal transmittance of the building envelope elements in the airconditioning consumptions. This paper analyzes the effect of different U-values on the building design in the Mediterranean area. 192 000 residential buildings were randomly generated and the energy consumption assessed for sixteen distinct locations. It was found that, in northern Mediterranean locations, as U-values decrease, the energy consumption also decreases. However, in warmer climates, low thermal transmittances tend to significantly increase the energy consumption. Hence, the lower the latitude, the higher the most adequate U-values, due to the increase of the cooling demands. Additionally, geometry-based indexes were correlated with the buildings energy performance. For high U-values it was found that bigger buildings worsen the energy performance and larger windows tend to improve it. For low U-values, bigger North-faced windows are beneficial. There is an adequate interval of values for which the geometry has a lower impact, which is wider and higher for lower latitudes, thus meaning that not only the building performance improves but architects are also freer to explore alternative designs.

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Section: Introductionmentioning

confidence: 99%

The impact of thermal transmittance variation on building design in the Mediterranean region

et al. 2019

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“…Troup et al [92] demonstrated that WWR is statistically significant at most of the levels of glazing when predicting the cooling, lighting, and ventilation energy use, but they are not statistically significant for the heating load in office buildings in the US. Marino et al [93] highlighted that the optimal value of WWR is not strongly influenced by the climate conditions, and also the insulation features of the envelope does not have a strong effect on this parameter, but instead the influence of WWR on the total energy consumption are associated with the operation mode of air conditioning systems, orientation of outside windows, and the glazing types of windows [88].…”

Section: Discussionmentioning

confidence: 99%

Energy-Efficient Window Retrofit for High-Rise Residential Buildings in Different Climatic Zones of China

Hossain

et al. 2019

Sustainability

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The building envelope plays a significant role in the energy performance of buildings and windows are a key element in transmitting heating and cooling between the indoor and outdoor environment, and hence an adequate window system is one of the most important retrofit strategies of existing buildings for energy conservation. Therefore, this study presents a method with a theoretical case study to examine the improvement of energy efficiency in a typical high-rise residential building through window retrofitting. A building energy design model in Designbuilder along with a building information modeling (BIM) model in Revit are developed, with 20 common potential glazing alternatives being analyzed to predict the potential energy savings in the same case building with identical orientation located in a variety of climate zones in China. Based on different parameters and considerations, the results demonstrated that the currently relatively expensive low-e window glazing has the best energy performance in all climate zones, but is sufficiently close to conventionally glazed windows in its energy efficiency to discourage its adoption at present, and that, instead, a single dark conventional glazed window is preferred in a hot summer/warm winter climate, double dark traditional glazing in a hot summer/cold winter climate, and a double clear conventional window in a cold climate. Based on the simulated results, an indicative suggestion was provided to select an adequate window system for residential building retrofitting in the studied climates or similar climatic regions.

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“…However, using too many variables for designing an ANN model is not always leading to accurate predication results and, of course, complicating the model [17]. Thus, in order to reduce the complexity of the ANN model and also to reduce the required computational efforts and time only six most effective variables are utilized for ANN design, which are chosen based on the reviewed papers [18][19][20] and preliminary analysis using HAP software. These variables are discussed in the following paragraphs.…”

Section: Input Variables To the Ann Modelmentioning

confidence: 99%

“…Another crucial constituent that needs to be considered for energy efficiency purposes is window system, due to the important role that it plays in solar gain management and heat exchange processes [20]. Thus, window to wall ratio (WWR), which is the ratio of the glazed surface to the gross facade area, has been considered as one of the important input variables to the ANN, since glass represents the weakest thermal component in the building due to its high U-Value.…”

Section: ) Window To Wall Ratio (Wwr)mentioning

confidence: 99%

Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network

Jaber¹,

Saleh²,

Ali³

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Predicating the required building energy when it is in the design stage and before being constructed considers a crucial step for in charge people. Hence, the main aim of this research is to accurately forecast the needed building cooling energy per hour for educational buildings at University of Technology in Iraq. For this purpose, the feed forward artificial neural network (ANN) has been selected as an efficient technique to develop such a predication system. Firstly, the main building parameters have been investigated and then only the most important ones were chosen to be used as inputs to the ANN model. However, due to the long time period that is required to collect actual consumed building energy in order to be employed for ANN model training, the hourly analysis program (HAP), which is a building simulation software, has been utilized to produce a database covering the summer months in Iraq. Different training algorithms and range of learning rate values have been investigated, and the Bayesian regularization backpropagation training algorithm and 0.05 learning rate were found very suitable for precise cooling energy prediction. To evaluate the performance of the optimized ANN model, mean square error (MSE) and correlation coefficient (R) have been adopted. The MSE and R indices for the predication results proved that the optimized ANN model is having a high predication accuracy with 5.99*10-6 and 0.9994, respectively.

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Does window-to-wall ratio have a significant effect on the energy consumption of buildings? A parametric analysis in Italian climate conditions

Cited by 127 publications

References 45 publications

The impact of thermal transmittance variation on building design in the Mediterranean region

The impact of thermal transmittance variation on building design in the Mediterranean region

Energy-Efficient Window Retrofit for High-Rise Residential Buildings in Different Climatic Zones of China

Prediction of Hourly Cooling Energy Consumption of Educational Buildings Using Artificial Neural Network

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