Multiscale modelling of urban climate

Climate has a direct impact on cities' energy flows due to the space conditioning (heating, cooling) needs of the buildings accommodated. This impact may be reinforced due to climate change and to the (so called) urban heat island effect. The corresponding changes in energy demands alter greenhouse gas emissions so that there is a feedback loop. To be able to simulate cities' metabolism with reasonable accuracy it is thus important to have good models of the urban climate. But this is complicated by the diverse scales involved. The climate in a city, for example, will be affected not only by the buildings within the urban canopy (the size of a few meters) but also by large topographical features such as nearby water bodies or mountains (the size of a few kilometers). Unfortunately it is not possible to satisfactorily resolve all of these scales in a computationally tractable way using a single model. It is however possible to tackle this problem by coupling different models which each target different climatic scales. For example a macro model with a grid size of 200 – 300 km may be coupled with a meso model having a grid of 0.5-1 km, which itself may be coupled with a micro model of a grid size of 5-10 meters. Here we describe one such approach. Firstly, freely available results from a macro-model are input to a meso-model at a slightly larger scale than that of our city. This meso-model is then run as a pre-process to interpolate the macro-scale results at progressively finer scales until the boundary conditions surrounding our city are resolved at a compatible scale. The meso-model may then be run in the normal way. In the rural context this may simply involve associating topography and average land use data with each cell, the former affecting temperature as pressure changes with height the latter affecting temperature due to evapo(transpir)ation from water bodies or vegetated surfaces. In the urban context however, it is important to account for the energy and momentum exchanges between our built surfaces and the adjacent air, which implies some representation of 3D geometry. For this we use a new urban canopy model in which the velocity, temperature and scalar profiles are parameterized as functions of built densities, street orientation and the dimensions of urban geometric typologies. These quantities are then used to estimate the corresponding sources and sinks of the momentum and energy equations. Even at the micro-scale the use of conventional computational fluid dynamics modeling is unattractive because of the time involved in grid generation / tuning and the definition of boundary conditions. Furthermore, even the simplest geometry may require hundreds of millions of grid cells for a domain corresponding to a single meso-model cell, particularly if unstructured grids are used. To overcome this problem we describe a new approach based on immersed boundaries in which the flow around any complex geometry can be computed using a simple Cartesian grid, so that users benefit from both improved productivity and accuracy. Thus, a completely coupled macro, meso and micro model can be used to predict the temperature, wind and pressure field in a city taking into account not only the complex geometries of its built fabric but also the scales which are bigger than the city itself. In this thesis we describe for the first time the theoretical basis of this new multiscale modeling approach together with examples of its application.

Novel models towards predictive control of advanced building systems and occupant comfort in buildings

Nikolaos Zarkadis

People in developed countries spend today most of their time inside buildings as part of the modern way of life. As a result, the building sector accounts for almost 40% of the total energy consumption and a big part of the energy bill goes to maintain the visual and thermal comfort of their occupants. At the same time, awareness is being raised during the last decades about the greenhouse gas emissions and the possibly irreversible effects of global warming; both linked to excessive use of non-renewable primary energy sources which still power most of the world, including our buildings. Thus, moderating the energy consumed in them is a top priority. However, this does not imply a horizontal cut in energy consumption that would result in a drop of user comfort. Instead, we suggest that improving energy efficiency in buildings while maintaining or even improving the user comfort is the optimal solution. It is indeed the core of this thesis that there is a great energy saving potential in refining the control of building systems such as electric lighting, heating, cooling and ventilation, which more than often consume a lot of energy without delivering the analogous amount of visual and thermal comfort. In this direction, this thesis proposes the development of a novel predictive control algorithm for the control of electrochromic glazing using a low cost sky scanner using a simple web camera. The developed algorithm demonstrated an average prediction accuracy of 92% and integrates and controls the blinds and electric lighting to maximise visual comfort taking into account outdoor and indoor conditions, presence and user actions. Measurements and extensive simulations showed that the elaborated algorithm improves thermal and visual comfort when compared to standard glazing coupled with blinds and exhibits acceptable levels of energy consumption for space heating and electric lighting. In the same subject of improving building control, a novel approach for controlling building systems by using state-based stochastic data-driven models to identify "season" is defined and developed. We reason that the season variable is unique to every building and it depends on weather conditions, user behaviour and building construction. The developed models identified "season" with an accuracy that ranged from 69 to 91% and it was shown through simulations that a controller based on Hidden Markov Models can reduce energy demand for heating and improve the thermal comfort of occupants in different building construction types. Finally, the use of Hidden Markov Models was further explored in this thesis by suggesting a novel model for the estimation of occupants' visual comfort in buildings. The proposed model is based on horizontal workplane illuminance measurements using ceiling-mounted sensors as well on vertical illuminance monitoring at the observer's eyes plane (pupillary illuminance) by means of wearable portable sensors. We argue that the proposed model improves greatly over the various existing discomfort glare indices and metrics and it is also convincingly demonstrated that it can be seamlessly integrated and used in building automation systems based on fuzzy logic.

EPFL2015

Simulating occupant presence and behaviour in buildings

Jessen Page

Various factors play a part in the energy consumption of a building : its physical properties, the equipment installed for its functioning (the heating, ventilation and air-conditioning system, auxiliary production of electricity or hot water), the outdoor environment and the behaviour of its occupants. While software tool designers have made great progress in the simulation of the first three factors, for the latter they have generally relied on fixed profiles of typical occupant presence and associated implications of their presence. As a result the randomness linked to occupants, i.e. the differences in behaviour between occupants and the variation in time of each behaviour, plays an ever more important role in the discrepancy between the simulated and real performances of buildings. This is most relevant in estimating the peak demand of energy (for heating, cooling, electrical appliances, etc.) which in turn influences the choice of technology and the size of the equipment installed to service the building. To fill this gap we have developed a family of stochastic models able to simulate the presence of occupants and their interactions with the building and the equipment present. A central model of occupant presence, based on an inhomogeneous Markov chain, produces a time series of the number of occupants within a predefined zone of a building. Given a weekly profile of the probability of presence, simplified parameters relating to the periods of long absence and the mobility of the person to be simulated, it has proven itself capable of reproducing that person's patterns of occupancy (times of first arrival, of last departure and periods of intermediate absence and presence) to a good degree of accuracy. Its output is used as an input for models for the simulation of the behaviour of occupants regarding the use of appliances in general, the use of lighting devices, the opening of windows and the production of waste. The appliance model adopts a detailed bottom-up approach, simulating each appliance with a black-box algorithm based on the probability of switching it on and the distribution of the duration and power of its use, whereas the interaction of the occupant with windows is determined by randomly changing environmental stimuli and the related thresholds of comfort randomly selected for each occupant. When integrated within a building simulation tool, these stochastic models will provide realistic profiles of the electricity and water consumed, the wastewater and solid waste produced and the heat emitted or rejected, both directly or indirectly by the occupant.

EPFL2007

Development and application of a numerical simulation system to evaluate the impact of anthropogenic heat fluxes on urban boundary layer climate

Andrea Krpo

Increasing economic development, and growing population, generated during the last decades a very important growth of cities. Urban regions include nowadays more than half of the global population and, by 2030, this proportion is forecasted to increase to three quarters. A consequent more and more extensive use of natural resources, together with increasing anthropogenic activities such as emissions from traffic and factories, or heating from air-conditioning facilities, modify local climate in many different ways, leading to a progressive degradation of life quality in urban areas. Taking these facts into account, there is nowadays a real need for efficient urban planning guidelines and sustainability policies in order to improve life quality in urban areas. Different points should be considered including urban warming, air pollution, human health, economic, and cooling energy needs. The present work goes in this direction, aiming at developing a simulation system for the study of the complex interactions between buildings and the atmosphere. The system of equations describing atmospheric flows is highly non linear and it is common to employ numerical techniques in order to solve them. Moreover, the representation of urban canopy climate, and related air pollution problems, requiree taking the interaction between urban scale (tens of kilometers), and mesoscale (hundreds of kilometers) processes into account. At first, starting from previous studies, a mesoscale meteorological model has been developed as part of this work. Urban induced processes have been considered by implementing inside the model a detailed urban parameterization scheme developed in a previous work. The scheme is able to capture different urban processes, and reproduces the effects of cities in a more accurate way than traditional methods usually used in mesoscale models. However, the heat generated in buildings, and the way this heat is exchanged with the exterior, was not explicitly resolved. In particular, recent studies indicated that anthropogenic heat from air-conditioning facilities can play an important role and should be taken into account for more complete urban climate studies. To this purpose, a Building Energy Model (BEM) has been developed, and coupled to the Urban Canopy Parameterisation (UCP). This building model takes into account the diffusion of heat through walls, roofs, and floors, the natural ventilation, the generation of heat from occupants and equipments, and the consumption of energy through air conditioning systems. Comparisons with other programs indicate that BEM is able to accurately simulate the basic heat transfer phenomena, and to reproduce the heat fluxes exchanged between buildings and the atmosphere. In a second part of the work, the simulation system composed by the Mesoscale Model (MM), UCP, and BEM is tested with respect to one, and two-dimensional configurations. In particular, the impact of BEM on the meteorological variables is analyzed, as well as the efficiency of different urban warming countermeasures, and cooling energy demands control strategies. Two-dimensional results are then utilized as guidance for the application of MM-UCP-BEM over the realistic configuration including the city of Basel and the surrounding areas. At first, comparisons of urban and rural simulated temperatures with measures provided by the BUBBLE project (Basel Urban Boundary Layer Experiment), have shown that the model could reasonably well reproduce the variations in outdoor air temperature. In a second time, the simulations considered for the two-dimensional configuration are applied to the case of Basel. Numerical results confirm that anthropogenic fluxes from air-conditioning facilities can have a non negligent impact on the urban meteorology. Typically, they modify the outdoor temperature and increase the Urban Heat Island (UHI) phenomenon. In the last part of the study, the applicability of the model in performing urban warming countermeasures, and cooling energy demands control strategies, is evaluated. In this purpose in mind, a sensitivity analysis is carried out indicating that appropriate physical properties of built materials, efficient air-conditioning systems, and the application of simple energy saving policies, can lead to very important cooling energy savings. In general, the application of BEM inside the UCP allows computing the heat released into the atmosphere by air-conditioning facilities, as well as the corresponding feedbacks produced on the different meteorological variables. It also increases the capability of the urban parameterisation to provide more detailed studies of urban warming countermeasures, and cooling energy demands in real cities.

EPFL2009