Tachyon Logamediate inflation on the brane

According to a Barrow's solution for the scale factor of the universe, the main properties of the tachyon inflation model in the framework of RSII braneworld are studied. Within this framework the basic slow-roll parameters are calculated analytically. We compare this inflationary scenario against the latest observational data. The predicted spectral index and the tensor-to-scalar fluctuation ratio are in excellent agreement with those of {\it Planck 2015}. The current predictions are consistent with those of viable inflationary models.Comment: 6 pages,3 figures, the paper has been accepted by EPJ

Using collective intelligence to enhance demand flexibility and climate resilience in urban areas

Collective intelligence (CI) is a form of distributed intelligence that emerges in collaborative problem solving and decision making. This work investigates the potentials of CI in demand side management (DSM) in urban areas. CI is used to control the energy performance of representative groups of buildings in Stockholm, aiming to increase the demand flexibility and climate resilience in the urban scale. 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 measure, which is extending the indoor temperature range. A simple platform and algorithm are developed for modelling CI-DSM, considering two timescales of 15 min and 60 min. Three climate scenarios are used to represent typical, extreme cold and extreme warm years in Stockholm. Several indicators are used to assess the performance of CI-DSM, including Demand Flexibility Factor (DFF) and Agility Factor (AF), which are defined explicitly for this work. According to the results, CI increases the autonomy and agility of the system in responding to climate shocks without the need for computationally extensive central decision making systems. CI helps to gradually and effectively decrease the energy demand and absorb the shock during extreme climate events. Having a finer control timescale increases the flexibility and agility on the demand side, resulting in a faster adaptation to climate variations, shorter engagement of buildings, faster return to normal conditions and consequently a higher climate resilience

Climate change and energy performance of European residential building stocks – A comprehensive impact assessment using climate big data from the coordinated regional climate downscaling experiment

In recent years, climate change and the corresponding expected extreme weather conditions have been widely recognized as potential problems. The building industry is taking various actions to achieve sustainable development, implement energy conservation strategies, and provide climate change mitigation. In addition to mitigation, it is crucial to adapt to climate change, and to investigate the possible risks and limitations of mitigation strategies. Although the importance of climate change adaptation is well-understood, there are still challenges in understanding and modeling the impacts of climate change, and the consequent risks and extremes. This work provides a comprehensive study of the impacts of climate change on the energy performances and thermal comfort of European residential building stocks. To perform an unbiased assessment and account for climate uncertainties and extreme events, a large set of future climate data was used for a 90-year period (2010–2099). Climate data for 38 European cities in five different climate zones, downscaled by the “RCA4” regional climate model, were synthesized and applied to simulate the respective energy performances of the residential building stocks in the cities. The results suggest that there will be larger needs for cooling buildings in the future and less heating demand; however, there are differences in the variation rates between zones and cities. Discomfort hours will increase notably in cities within cooling-dominated zones, but will not be affected considerably in cities within heating-dominated zones. In addition to long-term changes, climate-induced extremes can considerably affect future energy demands, especially the cooling demand; this may become challenging for both buildings and energy systems

High-resolution impact assessment of climate change on building energy performance considering extreme weather events and microclimate – Investigating variations in indoor thermal comfort and degree-days

Climate change and urbanization are two major challenges when planning for sustainable energy transition in cities. The common approach for energy demand estimation is using only typical meso-scale weather data in building energy models (BEMs), which underestimates the impacts of extreme climate and microclimate variations. To quantify the impacts of such underestimation on assessing the future energy performance of buildings, this study simulates a high spatiotemporal resolution BEM for two representative residential buildings located in a 600 7 600 m2 urban area in Southeast Sweden while accounting for both climate change and microclimate. Future climate data are synthesized using 13 future climate scenarios over 2010-2099, divided into three 30-year periods, and microclimate data are generated considering the urban morphology of the area. It is revealed that microclimate can cause 17% rise in cooling degree-day (CDD) and 7% reduction in heating degree-day (HDD) on average compared to mesoclimate. Considering typical weather conditions, CDD increases by 45% and HDD decreases by 8% from one 30-year period to another. Differences can become much larger during extreme weather conditions. For example, CDD can increase by 500% in an extreme warm July compared to a typical one. Results also indicate that annual cooling demand becomes four and five times bigger than 2010-2039 in 2040-2069 and 2070-2099, respectively. The daily peak cooling load can increase up to 25% in an extreme warm day when accounting for microclimate. In the absence of cooling systems during extreme warm days, the indoor temperature stays above 26\ub0C continuously over a week and reaches above 29.2\ub0C. Moreover, the annual overheating hours can increase up to 140% in the future. These all indicate that not accounting for influencing climate variations can result in maladaptation or insufficient adaptation of urban areas to climate change

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

Combining computational fluid dynamics and neural networks to characterize microclimate extremes: Learning the complex interactions between meso-climate and urban morphology

The urban form and extreme microclimate events can have an important impact on the energy performance of buildings, urban comfort and human health. State-of-the-art building energy simulations require information on the urban microclimate, but typically rely on ad-hoc numerical simulations, expensive in-situ measurements, or data from nearby weather stations. As such, they do not account for the full range of possible urban microclimate variability and findings cannot be generalized across urban morphologies. To bridge this knowledge gap, this study proposes two data-driven models to downscale climate variables from the meso to the micro scale in arbitrary urban morphologies, with a focus on extreme climate conditions. The models are based on a feedforward and a deep neural network (NN) architecture, and are trained using results from computational fluid dynamics (CFD) simulations of flow over a series of idealized but representative urban environments, spanning a realistic range of urban morphologies. Both models feature a relatively good agreement with corresponding CFD training data, with a coefficient of determination R2 = 0.91 (R2 = 0.89) and R2 = 0.94 (R2 = 0.92) for spatially-distributed wind magnitude and air temperature for the deep NN (feedforward NN). The models generalize well for unseen urban morphologies and mesoscale input data that are within the training bounds in the parameter space, with a R2 = 0.74 (R2 = 0.69) and R2 = 0.81 (R2 = 0.74) for wind magnitude and air temperature for the deep NN (feedforward NN). The accuracy and efficiency of the proposed CFD-NN models makes them well suited for the design of climate-resilient buildings at the early design stage

Towards realization of an Energy Internet: Designing distributed energy systems using game-theoretic approach

Distributed energy systems play a significant role in the integration of renewable energy technologies. The Energy Internet links a fleet of distributed energy systems to each other and with the grid. Interactions between the distributed energy systems via information sharing could significantly enhance the efficiency of their real-time operation. However, privacy and security concerns hinder such interactions. A game-theoretic approach can help in this regard, and enable consideration of some of these factors when maintaining interactions between energy systems. Although a game-theoretic approach is used to understand energy systems\u27 operation, such complex interactions between the energy systems are not considered at the early design phase, leading to many practical problems, and often leading to suboptimal designs. The present study introduces a game-theoretic approach that enables consideration of complex interactions among energy systems at the early design phase. Three different architectures are considered in the study, i.e., energy eystem prior to grid (ESPG), fully cooperative (FCS), and non-cooperative (NCS) scenarios, in which each distributed energy system is taken as an agent. A novel distributed optimization algorithm is developed for both FCS and NCS. The study reveals that FCS and NCS reduce the cost, respectively, by 30% and 15% compared to ESPG. In addition to cost reduction, there is a significant change in the energy system design when moving from FCS to NCS scenarios, clearly indicating the requirement for a scenario that lies between NCS and FCS. This will lead to reducing design costs while maintaining privacy

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