Dasaraden Mauree

and 2 more

Abstract Introduction (ATD) Energy requirements in urban areas are rising very fast and this trend is expected to continue with the increase in the urban population (70% by 2050). New buildings are built according to more and more stringent norms which can make them either net-zero or energy positive buildings. However, the core of the existing buildings is still old and renovation strategies and scenarios will not be able to make neighbourhoods or buildings in urban areas fully autonomous. Building optimization has been amply discussed in recent literature considering various aspects of the building especially considering the energy flow [1]. Lately, this has been extended considering building energy systems along with the various aspects of the building including location of windows, thickness of the walls, orientation etc. [2], [3]. However, this is a challenging due to its computationally demanding nature. When moving from building scale to neighbourhood scale, the complexity of the problem increases significantly [4].   A number of recent studies have focused on combining energy system sizing problem with building simulation in both urban and neighbourhood scale [5], [6]. However, most of these studies do not consider the impact adjacent buildings on the thermal and electricity demand (due to lighting). Effect of shadowing and boundary layer is not considered in most of the instances. The proper representation of buildings and their effects such as drag force effects, generation of turbulence etc. are crucial in the evaluation of building energy demand [7] as they can impact the convective heat transfer coefficient [8]. Hence, it is important to represent micro-climate accurately in the building simulation process which will be subsequently used for energy system sizing. More importantly, appropriate representation of micro-climate can be used along with building simulation and energy system sizing to optimally locate buildings with less thermal demand and more opportunity to integrate renewable energy technologies. This can lead to sustainable neighbourhoods with lower carbon impact.                                      In order to fill this gap, in this study we focus on combining building simulation model [9], [10], energy system sizing tool and an urban meteorological model [11] in order to assess the impact of building positioning on energy system designing in neighbourhood scale. We analyse the influence of using local climatic data and architectural aspects on energy demand of the building and subsequently to the energy system sizing for energy sustainable buildings. In the next section we describe the models used in the current study. We then show the results obtained and discuss them. In the final section we conclude on the most important findings in this study and give a few perspectives to further develop the simulation platform.   Overview of the computational platform and case studies (ATD) Computational model for urban micro climate and building simulation (Das & Silvia)The coupling of micro-scale model (such as CitySim) with meteorological models is essential to represent the impact of buildings on the climatic variables and to provide enhanced building energy simulation. Phenomenas such as the Urban Heat Island \cite{OKE_1982} are not represented in TMY or Meteonorm dataset since they are usually collected outside of the city. These data then need to be transformed to account for the specifities of the urban climate and to provide useful data to building energy models. This is why it is proposed here to use the CIM-CitySim coupled models and to extend it further.CIM - CitySim CIM is a 1D meteorological model \cite{Mauree_2017} that can work offline as a stand-alone module while using as input data from the climatic dataset (such as Meteonorm  \cite{Remund} ) or can be coupled with a 3D meteorological model (such as WRF ). CIM calculate high resolution vertical profiles of the variables considering the local environment (for example considering the presence of buildings and their density). The meteorological model is used to generate profiles wind speed, direction and air temperature profiles. CIM uses a diffusion equation derived from the Navier-Stokes equations but reduced to one direction only. It has subsequently been coupled with the CitySim building simulation software (see Figure 1) in order to determine the energy demand of a district \cite{Mauree_2017a}\cite{scartezzini2015}. Equations are taken from \citet{Mauree2017} to account for the obstacles density and height in the canopy. The coupling of CitySim and CIM provides enhanced boundary conditions for both models. On the one hand, CIM gets a calculation of the surface temperature from CitySim, which it then uses to simulate the flow in the column module and recalculate high resolution vertical profile of meteorological variables, such as the air temperature and the wind speed. On the other hand, CitySim is provided with local meteorological data. The modification of the variables influences two main processes that are computed by CitySim: the convective heat transfer coefficient (\(h_c\) \((W m^{-2}K^{-1})\)) given by:\(h_c=2.8+3U \) where \(U \) is the wind speed.  \(h_c\) is then used in the calculation of the flux \(Q_{ch}\):\(Q_{ch}= h_c (T_s-T_{air})\)where \(T_s \) is the surface temperature in K and \(T_{air}\) is the air temperature in K.the longwave exchanges. Indeed the longwave is calculated as a function of difference between the environmental temperature (\(T_{env} \)) and the surface temperature. The longwave flux \(Q_{lw}\) can be computed using : \(Q_{lw}=h_r (T_{env} - T_s)\)where \(h_r \) \((W m^{-2}K^{-1})\) is \(h_r= \epsilon_s \sigma \frac{1}{2}(T_{env}-T_s)\)where \(\epsilon\) is the emissivity of the surface and \(\sigma\) is the Stefan-Boltzmann constant.Figure \ref{211596} shows the flowchart used in the coupling between CIM and CitySim.