Field study on the energy consumption of school buildings in Luxembourg

Thewes, Andreas; Maas, Stefan; Scholzen, Frank; Waldmann, Danièle; Zürbes, Arno

doi:10.1016/j.enbuild.2013.10.002

Cited by 80 publications

(31 citation statements)

References 5 publications

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“…They identified this intervention as the principal measure to adopt in order to reach the level of efficiency of neighboring countries. Luxemburg's new schools have an average value of heating demand of 93 kWh/m 2 year [10] with potential energy savings in passive buildings ranging from 17% to 35% respectively, when compared with the standard ones.…”

Section: Introductionmentioning

confidence: 99%

“…[8,10]. Generally, space heating is not provided all day long but only in the morning during the teaching period.…”

Section: Introductionmentioning

confidence: 99%

“…Generally, space heating is not provided all day long but only in the morning during the teaching period. However, some schools also have afternoon activities with a longer time schedule for the heating system; • the ratio between external surface (S) and heated volume (V), which reflects how the geometric characteristics of the building influence energy use; • the construction period: new schools show lower energy use in comparison with buildings made during the absence of any energy regulation [10].…”

Section: Introductionmentioning

confidence: 99%

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Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting

et al. 2018

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A large part of the stock of Italian educational buildings have undertaken energy retrofit interventions, thanks to European funds allocated by complex technical-administrative calls. In these projects, the suggested retrofit strategies are often selected based on the common best practices (considering average energy savings) but are not supported by proper energy investigations. In this paper, Italian school buildings' stock was analyzed by cluster analysis with the aim of providing a methodology able to identify the best energy retrofit interventions from the perspective of cost-benefit, and to correlate them with the specific characteristics of the educational buildings. This research is based on the analysis of about 80 school buildings located in central Italy and characterized by different features and construction technologies. The refurbished buildings were classified in homogeneous clusters and, for each of them, the most representative building was identified. Furthermore, for each representative building a validating procedure based on dynamic simulations and a comparison with actual energy use was performed. The two buildings thus singled out provide a model that could be developed into a useful tool for Public Administrations to suggest priorities in the planning of new energy retrofits of existing school building stocks.

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Section: Introductionmentioning

confidence: 99%

“…[8,10]. Generally, space heating is not provided all day long but only in the morning during the teaching period.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting

et al. 2018

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“…The educational cluster was however found to dominate the sample average, with its buildings accounting for 73.5% of the heat demand, with an average of 207.4 kWh/m 2 /year. In Europe, studies show an average for schools of 100 kWh/m 2 /year (Thewes et al, 2014).…”

Section: Benchmarksmentioning

confidence: 99%

A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London

Tian

Choudhary

2012

Energy and Buildings

102

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Evaluación de la demanda energética de edificios no residenciales en Escocia. Energy demand benchmarking of non-domestic buildings in Scotland.Resumen-Con los años el rendimiento energético del edificio se ha convertido en una preocupación predominante para los propietarios y administradores de bienes raíces. La atención se centra generalmente en edificios de viviendas, pero en los últimos veinte años un interés en edificios no residenciales ha surgido en el Reino Unido. Los puntos de referencia general se pueden encontrar a escala del Reino Unido, aunque a menudo está restringido a Inglaterra y Gales. Este documento tiene como objetivo proporcionar puntos de referencia para el parque inmobiliario no doméstico escocés como parte del Ayuntamiento de Edimburgo. En esta investigación, la muestra seleccionada incluye datos de energía y las emisiones de carbono calculadas de 199 edificios.Los parámetros decisivos fueron la intensidad de uso de la energía (kWh/m2) y el uso y la edad de los edificios. Esto permitió la creación de seis tipos de edificios, aunque siguiendo patrones de ocupación se dividió en cuatro categorías desde el s. XVI hasta el s. XXI. Los principales resultados revelan el predominio de un clúster de edificios educativos en términos de superficie (72%), el número de edificios (70%), las emisiones de carbono (68% de los cerca de 42.000 toneladas de CO2) y el consumo de energía (61% de la 38,4 MWh de electricidad consumida, y el 73% del 117,4 MWh de gas natural que se consume). Entre estos niveles de consumo destacan el potencial de ahorro de energía para las escuelas: 186 kWh / m2 / año en promedio, en comparación con la media europea de 100 kWh / m2 / año de energía térmica de uso final. Palabras clave-No doméstica; demanda energética; evaluación; Escocia.Abstract-Over the years building energy performance has become a predominant concern for owners and real estate managers. The focus is usually on residential buildings but in the last twenty years an interest in non-domestic buildings has emerged in the UK. Benchmarks can generally be found at UK scale, although often restricted to England and Wales. This paper aims to provide benchmarks for the Scottish non-domestic building stock as part of the City of Edinburgh Council estate. In this research, the selected sample includes energy data and calculated carbon emissions of 199 buildings. The deciding parameters were the energy use intensity (kWh/m 2 ) and the use and age of buildings. The last two allowed the creation of six clusters in which to group buildings of similar occupancy patterns in four age categories from the 16th to the 21st century. The main findings reveal the predominance of an educational buildings cluster in terms of floor area (72%), number of buildings (70%), carbon emissions (68% of about 42,000 tons of CO 2 ), and energy consumption (61% of the 38.4 MWh of electricity consumed, and 73% of the 117.4 MWh of natural gas consumed). These levels of consumption highlight the energy saving potential for schools: 186 kWh/m2/year on a...

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“…Since buildings contribute 20-40% of energy use worldwide [1], efforts to decrease their energy consumption are essential. Advances in buildings' sustainability can reduce their energy consumption by improving building envelopes; and HVAC systems.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of using WiFi technologies to detect and predict building occupancy

et al. 2017

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Abstract. This paper presents findings of a case-study demonstrating the effectiveness of using WiFi networks to detect occupancy as opposed to CO 2 sensors, commonly used for demand-controlled heating, ventilation and air conditioning (HVAC) systems. The study took place in one building at the University of Manitoba Fort Garry campus in Canada. In a classroom, the number of WiFi connections was collected on an hourly basis over one-week, simultaneously with CO 2 concentration levels at 10-min intervals. The number of occupants in this classroom was also counted on an hourly basis over the same study period. Data analysis showed that WiFi counts predicted actual occupancy levels more accurately than CO 2 concentration levels, thus validating the use of this technology to track occupancy. This study was the first to use both CO 2 concentration and WiFi counts simultaneously as indicators for occupancy. Results demonstrated the possibility of using WiFi counts in large buildings for controlling HVAC systems at a higher accuracy and lower cost than other sensor technologies. Implications and influences: Given the large contribution of HVAC systems to overall buildings' energy consumption, this study presents a new method for efficiently operating HVAC systems. Results highlighted the accuracy of using WiFi connections as predictors for occupancy patterns to be used for controlling HVAC systems instead of CO 2 sensors. These findings provide a foundation for further research on using WiFi networks to manage and operate HVAC systems in new buildings. Efficient operation of these systems based on real-time occupancy as opposed to static schedules provides facility managers with an opportunity for significant energy savings at a relatively low cost.Keywords: buildings energy management / occupancy and energy consumption / smart buildings systems / green buildings / sensor-based HVAC systems

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Field study on the energy consumption of school buildings in Luxembourg

Cited by 80 publications

References 5 publications

Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting

Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting

A probabilistic energy model for non-domestic building sectors applied to analysis of school buildings in greater London

Effectiveness of using WiFi technologies to detect and predict building occupancy

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