Climate Simulation of an Attic Using Future Weather Data Sets - Statistical Methods for Data Processing and Analysis

The effects of possible climate changes on a cold attic performance are considered in this work. The hygro-thermal responses of the attic to different climate data sets are simulated using a numerical model, which has been made using the International Building Physics Toolbox (IBPT). Cold attic, which is the most exposed part of the building to the environment, is classified as a risky construction in Sweden. Mould growth on internal side of the attic roof, due to condensation of water vapor from the surrounding environment has been increasing over the last decade, and thereby the risk for degrading the performance of construction. The attic studied in this work is a naturally ventilated space under a pitched roof on top of a 2 storey building. Climate inside the attic has been simulated using different weather data sets for the period of 1961-2100 in four cities of Sweden: Gothenburg, Lund, Stockholm and Östersund. The weather data sets, which are the results of climate simulations, enclose different uncertainties. The uncertainties related to differences in spatial resolutions, global climate models (GCMs), CO2 emission scenarios and initial conditions are considered here. At the end enormous climate data sets are used in this study. Analysis of the long term climate data demands suitable statistical methods. Two methods have been applied from meteorology: a nonparametric method for assessing the data without tracking of time, and a parametric method for decomposition of the parameter variabilities into three constructive parts. Looking into the decomposed components of the parameter and its variabilities enables to analyze the data with different time resolutions. Applying the selected statistical methods helps in understanding of the importance of different uncertainties of the weather data and their effects on the attic simulation

Hygrothermal Simulations of Buildings Concerning Uncertainties of the Future Climate

Global warming and its effects on climate are of great concern. Climate change can affect buildings in different ways, i.e. it can change the energy demand or the moisture durability of buildings in the future. In Sweden, most of the last 20 years have been mild and wet compared to the 1961-1990 climate reference period. Future needs and risks of the building sector depend on the future climate which can be simulated by climate models. It is possible to assess the future conditions of buildings using simulated climate data. Since climate models are not certain there exist different scenarios for the future climate. Impact assessment of the climate change on buildings in Sweden has been performed in this study. The hygrothermal conditions of attics and the energy performance of buildings in Sweden were simulated. The study was mainly based on comparative analysis of different scenarios, buildings and periods. Four attic constructions, and the building stocks of four cities, were studied considering 12 climate scenarios for the period of 1961-2100 and one reference scenario for the period of 1961-2005. Future climate data sets were generated by the global climate models (GCMs) which were downscaled using regional climate models (RCMs) at the Rossby Centre at the Swedish Meteorological Hydrological Institute (SMHI). Climate scenarios were selected in a way to assess climate data uncertainties caused by different GCMs, RCMs, emissions scenarios, initial conditions and spatial resolutions. With the help of different statistical methods, uncertainties of the climate data and their effects on the hygrothermal simulations were analysed in different time scales. According to the results of this work, a reliable impact analysis of the climate change cannot be based on a few number of climate scenarios. Uncertainties of the climate data can affect the building simulation results considerably. Depending on the case, some uncertainty factors of the climate data might be neglected, however it depends on the building construction, the phenomenon and the season that are considered. Among the climate uncertainties which were studied in this work, the uncertainty caused by GCMs affected the hygrothermal simulations the most. The Swedish building sector can gain or recede from changes in climate; the heating demand of buildings will decrease by having warmer climate but the moisture problems will increase by having more humid climate. Results point to an increment of the moisture problems in attics. The absolute safe case for preventing mould growth is using controlled mechanical ventilation in attics which consumes energy. The energy simulations of the building stocks showed that the heating demand and its variations will decrease in the future. Comparing the indoor temperature in buildings with and without mechanical cooling system showed that there is no substantial need for increased mechanical cooling in the future

Impacts of climate change and its uncertainties on the renewable energy generation and energy demand in urban areas

This work investigates the effects of future climate uncertainties in calculating the heating and cooling demand of buildings and estimating potentials for renewable energy generation (solar PV and wind). The building stock of Lund in Sweden is considered for energy simulations and for future climate, the most recent outputs of RCA4, which is the 4th generation of the Rossby Centre regional climate model (RCM), is used considering several two representative concentration pathways (RCPs) and four global climate models (GCMs). Simulations and assessment are performed for three 30-year time periods, from 2010 until 2099. Through comparing distributions of data sets, it is found that the uncertainty induced by climate models affects the estimation of renewable energy generation more than those induced by time periods. Changes in the heating demand due to climate change and uncertainties are surprisingly low while it is very large for cooling demand. This can be because of having a good quality for buildings on the average, however this should be more investigated for other cities in Sweden

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Application of typical and extreme weather data sets in the hygrothermal simulation of building components for future climate – A case study for a wooden frame wall

A method for synthesizing representative weather data for future climate out of regional climate models (RCMs) was introduced previously for the energy simulation of buildings (Nik, 2016). The method suggests creating one typical and two extreme data sets based on the distribution of the outdoor dry bulb temperature (Tdrybulb). This article extends the application of such weather data in the hygrothermal simulation of buildings by simulating a pre-fabricated wooden frame wall. To investigate the importance of considering moisture and rain conditions in creating the representative weather files, two more groups of weather data are synthesized based on the distribution of the equivalent temperature (Tequivalent) and rain. Moisture content, relative humidity, temperature and mould growth rate are calculated in the façade and insulation layers of the wall for several weather data sets. Results show that the synthesized weather data based on Tdry bulb predict the hygrothermal conditions inside the wall very similar to the original RCM weather data and there is no considerable advantage in using the other two weather data groups. This study confirms the applicability of the synthesized weather data based on Tdry bulb and emphasizes the importance of considering extreme scenarios in the calculations. This enables having distributions more similar to the original RCM data while the simulation load is decreased enormously

The uncertainties in simulating the future hygro-thermal performance of an attic related to global climate models

Climate changing has been a debated subject during the last few years. Several future climate predictions have been generated based on numerical modeling. There are differences between climate data sets depending on the driving global climate models, initial conditions, emission scenarios, regional climate models, etc. Each future climate is based on some assumptions and consequently has some uncertainties. These uncertainties are dragged to the building simulation results by using the climate data for assessing the future performance of buildings. In this paper the uncertainties connected to having different global climate models are studied. The analysis is given for the city of Stockholm in Sweden and for the period from 1961 to 2100. The indoor climate of a cold attic has been simulated for the climate conditions of four different global climate models. Temperature and relative humidity of the climate have been analyzed using the method of decomposition of variabilities. The method helps to compare different climate and simulation results in long time periods. The comparison guides to understand the importance and effectiveness of outdoor climate variability components on the indoor climate. Sensitivity of the simulation results to different global climate models is illustrated when the indoor response to the long term and short term changes of the outdoor climate is considered

Empowering energy flexibility and climate resilience using collective intelligence based demand side management (CI-DSM)

This work investigates the effectiveness of Collective intelligence (CI) in demand side management (DSM) in urban areas to cope with extreme climate events. CI is a form of distributed intelligence that emerges in collaborative problem solving and decision making. It is used in a simulation platform to control the energy performance of buildings in an urban area in Stockholm, through developing CI-DSM and setting certain adaptation measures, including phase shifting in HVAC systems and building appliances. CI-DSM is developed based on a simple communication strategy among buildings, using forward (1) and backward (0) signals, corresponding to applying and disapplying the adaptation measures. The performance of CI-DSM is simulated for three climate scenarios representing typical, extreme cold and extreme warm years in Stockholm. According to the results, CI-DSM increases the autonomy and agility of the system in responding to climate shocks without the need for computationally extensive central decision making systems. CI-DSM helps to gradually and effectively decrease the energy demand and absorb the shock during extreme climate events

Assessing the climate change adaptation over four European cities

In recent years, climate change has been widely recognized as a potential problem. The building industry is taking a variety of actions towards sustainable development and climate change mitigation, such as retrofitting buildings. More than mitigation, it is important to account for climate change adaptation and investigate the probable risks and limits for mitigation strategies. For example, one major challenge may become achieving low energy demand without compromising indoor thermal comfort during warm seasons. This work investigates the future energy performance and indoor thermal comfort of four European cities belonging to four different climate zones in Europe; Barcelona, Koln, Brussels, and Copenhagen. An ensemble of future climate scenarios is used, including thirteen climate scenarios considering five different general circulation models (GCM) and three representative concentration pathways (RCP 2.6, RCP 4.5 and RCP 8.5). Through simulating the energy performance of the representative buildings in each city and considering several climate scenarios, this paper provides a comprehensive picture about the energy performance and indoor thermal comfort of the buildings for near-term, medium-term, and long-term climate conditions

Using Typical and Extreme Weather Files for Impact Assessment of Climate Change on Buildings

Considering climate change and assessing its impacts is challenging due to dealing with large data sets and uncertainties. This paper discusses an approach for the impact assessment of climate change based on synthesizing weather data sets out of several climate scenarios, in a way to generalize the assessment despite of the existence of climate uncertainties. The is based on creating one-year weather data, representing typical, extreme-warm and -cold conditions for 30-year periods, which results in decreasing the length of simulations enormously. The usefulness and accuracy of the results are discussed for energy and moisture simulations in buildings

Interactions between extreme climate and urban morphology : Investigating the evolution of extreme wind speeds from mesoscale to microscale

This paper investigates the interactions between urban morphology indicators and extreme weather variables. In this regard, variations of wind speed and air temperature at the urban microscale are studied for three urban morphologies by means of numerical simulations. Each urban model contains ninety-nine calculation points at different locations and heights to assess the variations during two 24-h cycles of extreme low and high wind speeds by introducing a microscale indicator. According to the results, transforming from mesoscale to microscale can considerably dampen the magnitude of wind speed (up to 66%) and amplify the air temperature (up to 39%). Moreover, the urban morphology parameters (layout geometry, final height and urban density) can change the average magnitude of wind speed (up to 23%) and air temperature (up to 16%) at microscale. For extreme low wind speeds (0.16–1.14 m/s), strong correlations exist between the mesoscale and microscale magnitude of wind speed and air temperature, while there is no significant correlation for extreme high wind speeds (12.2–14 m/s). For extreme low wind speeds, stronger buoyancy effects are observed at the urban canopies. An easy-to-setup approach is proposed to count for microscale conditions during extreme low wind speeds in urban climate studies