Assessment of overheating risk and the impact of natural ventilation in educational buildings of Southern Europe under current and future climatic conditions

Heracleous, C.; Michael, Aimilios

doi:10.1016/j.energy.2018.10.051

Cited by 69 publications

(33 citation statements)

References 43 publications

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“…They found that a reduction at a level of respectively 62%, 72% and 44% for the heating demand, cooling demand, and overheating rate could be achieved when the passive house concept was applied under the Portuguese climate. Heracleous et al [7] carried out the assessment of overheating risk and the impact of natural ventilation in educational buildings of Southern Europe under current and future climate conditions. It was found that natural ventilation alone could achieve a reduction of overheating hours where operative temperature exceeded the Chartered Institution of Building Services Engineers (CIBSE) maximum limits.…”

Section: Introductionmentioning

confidence: 99%

Overheating risk of a typical Norwegian residential building retrofitted to higher energy standards under future climate conditions

Tian

Hrynyszyn

2020

E3S Web Conf.

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Heat waves had attacked Norway in 2018 and 2019. Residents in Norway experienced a very uncomfortable indoor environment in the summer of 2018 and 2019. Few publications on the overheating risk of a typical Norwegian residential building under future climate change conditions were found. The typical Norwegian residential building retrofitted according to the minimum Norwegian energy requirements in the Norwegian Building Act of 2010 (Pbl2010/TEK17) and the international EnerPHit Standard was modelled in this study. Overheating risk of the typical residential building was simulated by Energy Plus engine via Ladybug and Honeybee plugins based on the Grasshopper. Overheating hours of the studied rooms under present-day, the 2050s and the 2080s weather conditions were shown. Too good airtightness does increase the overheating risk of the building when retrofitted to higher energy standards. It was found in this study that better insulation does reduce the overheating hours of the bedrooms slightly. This may be caused by low g-value of the windows based on recommendation according to the EnerPHit Standard. Overheating should be paid more attention in term of the expected future climate conditions. These results can provide some references to the buildings retrofitted to high-performance buildings.

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Section: Introductionmentioning

confidence: 99%

Overheating risk of a typical Norwegian residential building retrofitted to higher energy standards under future climate conditions

Tian

Hrynyszyn

2020

E3S Web Conf.

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“…However, the importance of prediction for residential building should not be neglected, because of the high proportion of the amount of residential buildings in the total buildings. In addition, the educational building and government building have the similar features with commercial building, thus, their energy consumption is easy to be predicted (Al-Saadi et al., 2017; Heracleous and Michael, 2018).…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Liu

et al. 2019

Energy Exploration & Exploitation

114

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It is of great significance to achieve the prediction of building energy consumption. However, machine learning, as a promising technique for many practical applications, was rarely utilized in this field. The most important reason is that the predictive structure with best performance is difficult to be determined. To fill the gap, this paper offers one in-depth review, which focuses on the accuracy analyses and model comparisons. Specifically, the accuracy analyses were conducted based on different types of buildings (e.g. residential building, commercial building, government building or educational building), different type of temporal granularity (e.g. sub-hourly, hourly, daily or annual), as well as input/output variables and historical data collections. Further, artificial neural network (ANN) and support vector machine (SVM), as the epidemic models, were compared in terms of their complexity of prediction processes, accuracies of results, the amounts of required historical data, the numbers of inputs, etc. Then the hybrid and single machine learning methods were outlined and compared in terms of their strengths and weaknesses. In addition, several vital defects and further research directions are presented from a multivariate perspective. We hope

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“…Gil-Báez et al [54] analysed the use of natural ventilation in school buildings in southern Spain, saving the primary energy consumption between 18 and 33% by using natural ventilation. A similar study by Heracleous and Michael [55] determined that the use of natural ventilation would reduce the risk of overheating in future climate change scenarios. Fernandes et al [56] analysed the effect of ventilation on the energy consumption of 500 virtual prototypes of buildings located in the Mediterranean, obtaining significant savings in the energy consumption of the virtual prototypes.…”

Section: Introductionmentioning

confidence: 92%

Analysing natural ventilation to reduce the cooling energy consumption and the fuel poverty of social dwellings in coastal zones

2020

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Assessment of overheating risk and the impact of natural ventilation in educational buildings of Southern Europe under current and future climatic conditions

Cited by 69 publications

References 43 publications

Overheating risk of a typical Norwegian residential building retrofitted to higher energy standards under future climate conditions

Overheating risk of a typical Norwegian residential building retrofitted to higher energy standards under future climate conditions

Accuracy analyses and model comparison of machine learning adopted in building energy consumption prediction

Analysing natural ventilation to reduce the cooling energy consumption and the fuel poverty of social dwellings in coastal zones

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