Local climate impact on the energy demand: an analysis at the European scale

A reduction by 80% of the greenhouse gas emissions as well as a similar decrease for the final energy consumption is needed to respect the energy strategies and climate objectives at the European scale. To address this issue, we analysed the energy demand of 17 European cities using the coupled CIM-CitySim model. Simulations taking into account the urban microclimate were performed for one typical year to obtain the cooling and the heating demand. When comparing the results with and without the urban microclimate, although on a yearly basis there does not seem to be much difference on the overall energy demand (60%). In the future, strategies to design more sustainable urban areas at the European scale will be evaluated. The objective is to be able to define mitigation strategies that will decrease the footprint of urban areas while at the same time increasing its liveability.

Silvia Coccolo, Cynthia Houmani, Ismaël Krafess, Dasaraden Mauree, Amarasinghage Tharindu Dasun Perera, Jean-Louis Scartezzini

IOP Science2019

On the Adaptation of Building Controls to the Envelope and the Occupants

David Daum

The sun is the biggest known source of energy in our solar system. We feel its strength when it gets hot during the the day and we notice its absence during the night when we feel cold. So as to be less dependent on the sun as an energy source, we implemented additional heating and cooling sources to maintain the temperature within a comfortable range. The downside to this is that the majority of energy consumed within the housing sector is used up on the heating and cooling alone. To profit from the vast energy source of the sun we propose a user-adaptive and building-adaptive blind control for residential buildings, that is implemented in prefabricated modules for facade renovation. User-adaptive means that it is the occupant who is responsible for the temperature control within the home. Building-adaptive, in this context, means that the temperature control is established automatically without any user input. Through the evaluation of occupant queries we have shown that a general measure for thermal comfort is not possible for all occupants. Consequently, there is a need for a personalized measure of thermal comfort. In order to create this the occupant enters votes via the interface; from this we deduced statistically the probability of comfort relative to the indoor temperature. According to the profile the control sets its target temperature. The profile steadily adapts the user's preferences and through this we can also capture seasonal changes in comfort temperature. This guarantees that at each point in time the control system knows the desired temperature and is taking action to achieve it. The adaption to the building is achieved with the fitting of a simple thermal building model with data collected by the sensors of the control system. We showed that the monitored data sufficiently fits the model. With the help of the simple model we evaluated different control strategies and optimized them according to the thermal profile. For our performance tests we conducted computer simulations as well as a 6-month field study. For the simulations, a specific test bed was suggested that would assess the saving potential, which can then be compared to the performance of the tested control. Results showed that the suggested control system is capitalizing on most of the achievable energy savings and thermal comfort. A 6-month field study in the LESO-PB building was carried out to test the impact on energy demand as well as comfort under real conditions. It appeared that the automatically controlled office needed only approximately 50% of the average heating energy that was used in the manually controlled offices. Furthermore, the probability of thermal comfort was, on average, 10% higher in the automatically controlled offices when compared to those that were controlled manually.

EPFL2011

Modelling the Urban Microclimate and its Influence on Building Energy Demands of an Urban Neighbourhood

Jérôme Henri Kämpf

In the past decades the portion of the population living in urban areas has continuously increased. Due to the high building density, the microclimate in urban areas changed significantly compared to rural areas. The temperatures measured in urban areas are, due to the urban heat island (UHI) effect, higher compared to the rural temperatures. The UHI intensities are increasing with higher building densities and growing cities. Space cooling and heating demands of buildings are strongly affected by the local microclimate at the building sites. Due to the climate change and the limited energy resources, energy saving and sustainability are nowadays important issues. A significant part of the global energy consumption is used for space cooling and space heating of buildings. Thus its minimization for buildings in urban areas has great energy saving potential. Most building energy simulation (BES) models were developed for stand-alone buildings and therefore do not consider effects of the urban microclimate. This can lead to inaccurate predictions of the space cooling and heating demands for buildings in urban areas. The aim of this paper is to investigate the urban microclimate and its potential influence on the energy demand of buildings in an urban context by conducting detailed flow, radiation and building energy simulations at the urban neighborhood scale. CitySim is used for the radiation and building energy simulations. In CitySim detailed radiation models for solar and longwave radiation are implemented that can account for the radiation exchange between neighbouring buildings. The flow around the buildings is modelled by running CFD (computational fluid dynamics) simulations using OpenFOAM. As a result it is shown, how the temperatures and wind speeds can strongly differ within different urban areas. Further an approach is presented, to consider the local microclimate in the building energy simulation tool CitySim.