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High energy use for space conditioning in residential buildings is a significant economic factor for owners and tenants, but also contributes to resource depletion and carbon emissions due to energy generation. Many existing dwellings should thus be retrofitted in order to fulfil the ambitious EU carbon emission mitigation goals by 2050. To investigate how future climate resilience can be implemented in the design process of retrofitting measures, this study concentrates on real case studies that have been retrofitted during the past decade. The performance of retrofitting measures for four case studies in Denmark and Germany were investigated under future climate projections and compared between the non-retrofitted initial stage of the buildings and the retrofitted stage. Building performance simulations were employed to investigate how severe the effects of climate change until the end of the 21st century on the material choice and system design is. Results show that summertime thermal comfort will be a major challenge in the future. Energy use for space heating was seen to decrease for periods in the future, also the severity of cold events decreased, resulting in a decline of heating peak loads. Additionally, not considering extreme events was proven to lead to miss-dimensioning thermal systems. Overall, the study shows that adaptation of informed decisions, accounting for the uncertainties of future climate, can bring a significant benefit for energy-efficient retrofits, potentially promoting adequate passive measures as well as free cooling to prevent overheating and enhance heat removal.

This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization. A surrogate model is developed with the support of a supervised learning technique (by using artificial neural network) in order to bypass computationally intensive Actual Engineering Model (AEM). Eight different neural network architectures are considered in the process of developing the surrogate model. Subsequently, a hybrid optimization algorithm (HOA) is developed combining Surrogate and AEM in order to speed up the optimization process while maintaining the accuracy. Pareto optimization is conducted considering Net Present Value and Grid Integration level as the objective functions. Transfer learning is used to adapt the surrogate model (trained using supervised learning technique) for different scenarios where solar energy potential, wind speed and energy demand are notably different. Results reveal that the surrogate model can reach to Pareto solutions with a higher accuracy when grid interactions are above 10% (with reasonable differences in the decision space variables). HOA can reach to Pareto solutions (similar to the solutions obtained using AEM) around 17 times faster than AEM. The Surrogate Models developed using Transfer Learning (SMTL) shows a similar capability. SMTL combined with the optimization algorithm can predict Pareto fronts efficiently even when there are significant changes in the initial conditions. Therefore, STML can be used along with the HOA, which reduces the computational time required for energy system optimization by 84%. Such a significant reduction in computational time enables the approach to be used for energy system optimization at regional or national scale.

Higher availability of future climate data sets, generated by regional climate models (RCMs) with fine temporal and spatial resolutions, improves and facilitates the impact assessment of climate change. Due to significant uncertainties in climate modeling, several climate scenarios should be considered in the impact assessment. This increases the number of simulations and size of data sets, complicating the assessment and decision making. This article suggests an easy-to-use method to decrease the number of simulations for the impact assessment of climate change in energy and building studies. The method is based on synthesizing three sets of weather data out of one or more RCMs: one typical and two extremes. The method aims at decreasing the number of weather data sets without losing the quality and details of the original future climate scenarios. The application of the method is assessed for an office building in Geneva and the residential building stock in Stockholm.Results show that using the synthesized data sets provides an accurate estimation of future conditions. Variations and uncertainties of future climate are represented by the synthesized data. In the case of synthesizing weather data using multiple climate scenarios, the number of simulations and the size of data sets are decreased enormously. Combining the typical and extreme data sets enables to have better probability distributions of future conditions, very similar to the original RCM data.

The objective of this paper is to optimize the passive design of newly-built residential buildings in hot summer and cold winter region of China for improving indoor thermal comfort while reducing building energy demand. In this respect, this paper investigates the performance of a representative apartment building in the city of Shanghai and evaluates the optimum solutions by using a developed optimization approach, which includes three major steps of 1) setting the model for multi-objective optimization, 2) sensitivity analysis for reducing the dimension of input variables, and 3) multi-objective optimization by using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) coupled with the Artificial Neural Network (ANN), among which a novel indicator for evaluating the annual indoor thermal comfort of residential buildings of Shanghai named Comfort Time Ratio (CTR) is defined based on the modification of Szokolay's theory in terms of bioclimatic analysis, and the impacts of passive design variables on the indoor thermal comfort and building energy demand in terms of different directions are comprehensively investigated. Results of the multi-objective optimization indicate that the residential buildings of Shanghai have a great potential in comfort-improvement and energy-saving. A series of novel optimal passive design tactics for residential buildings in Shanghai are derived accordingly which could be easily understood and conveniently carried out by the architects in practice.

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 facade 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.

Patterns of future climate and expected extreme conditions are pushing design limits as recognition of climate change and its implication for the built environment increases. There are a number of ways of estimating future climate projections and creating weather files. Obtaining adequate representation of long-term patterns of climate change and extreme conditions is, however, challenging. This work aims at answering two research questions: does a method of generating future weather files for building performance simulation bring advantages that cannot be provided by other methods? And what type of future weather files enable building engineers and designers to more credibly test robustness of their designs against climate change? To answer these two questions, the work provides an overview of the major approaches to create future weather data sets based on the statistical and dynamical downscaling of climate models. A number of weather data sets for Geneva were synthesized and applied to the energy simulation of 16 ASHRAE standard reference buildings, single buildings and their combination to create a virtual neighborhood. Representative weather files are synthesized to account for extreme conditions together with typical climate conditions and investigate their importance in the energy performance of buildings. According to the results, all the methods provide enough information to study the long-term impacts of climate change on average. However, the results also revealed that assessing the energy robustness of buildings only under typical future conditions is not sufficient. Depending on the type of building, the relative change of peak load for cooling demand under near future extreme conditions can still be up to 28.5% higher compared to typical conditions. It is concluded that only those weather files generated based on dynamical downscaling and that take into consideration both typical and extreme conditions are the most reliable for providing representative boundary conditions to test the energy robustness of buildings under future climate uncertainties. The results for the neighborhood explaining the critical situation that an energy network may face due to increased peak load under extreme climatic conditions. Such critical situations remain unforeseeable by relying solely on typical and observed extreme conditions, putting the climate resilience of buildings and energy systems at risk.

This work describes the research conducted in order to assess possible changes and uncertainties in future energy performance of the residential building stock in Stockholm. The investigation is performed on a sample of 153 existing and statistically selected buildings and covers the period of 1961–2100. Four uncertainty factors of the climate have been considered: global climate models, regional climate models, emissions scenarios and initial conditions; thereby, 12 different scenarios have been created. Energy performance of the building stock is studied by looking at the overall heating and cooling demand and the indoor temperature. Three cooling strategies of the building stock were evaluated: natural, natural and mechanical (hybrid mode) and only mechanical. To decrease the number of simulations, a method for sampling the climate data has been developed and tested against Sobol quasi-random sampling method. Results of the investigation show that for all the climate scenarios the future heating demand will decrease at the end of the studied period, i.e. around 30 kWh/m2 (30%) lower than before 2011, while the cooling demand will increase. Results for the heating demand can differ for about 30% between the scenarios and even more for the cooling demand. Since the current and future cooling demands are rather low, the natural cooling can be the safe choice for mitigating overheating. Uncertainties of the climate data can affect the energy simulation results, but it is possible to rank them and introduce margins to the design based on the importance of the uncertainty factor.

Most of the last 20 years in Sweden have been mild and wet compared to the 1961–1990 climate reference period. After a few relatively cold years in the mid-1980s, practically all years have been warmer than the preceding 30 years average. During the indicated period, an increase of moisture-related problems (mould growth) was observed in ventilated attics, a moisture sensitive building part. This work investigates hygrothermal performance of ventilated attics in respect to possible climate change. Hygrothermal simulations of attics were performed numerically in Matlab. Four attic constructions are investigated – a conventional attic and three alternative constructions suggested by practitioners. Simulations were done for the period of 1961–2100 using the weather data of RCA3 climate model. Effects of three different emissions scenarios are considered. Hygrothermal conditions in the attic are assessed using a mould growth model. Based on the results the highest risk of mould growth was found on the north roof of the attic in Gothenburg, Sweden. Results point to increment of the moisture problems in attics in future. Different emissions scenarios do not influence the risk of mould growth inside the attic due to compensating changes in different variables. Assessing the future performance of the four attics shows that the safe solution is to ventilate the attic mechanically, though this solution inevitably requires extra use of electrical energy for running the fan. Insulating roofs of the attic can decrease the condensation on roofs, but it cannot decrease the risk of mould growth considerably, on the wooden roof underlay.

This work investigates the prospective impacts of climate change on wind-driven rain (WDR) and walls through simulating the hygrothermal performance of rain screen of common vertical wall constructions for the climatic conditions of Gothenburg in Sweden. While a substantial amount of work has been done on the impact of climate change on the thermal performance of buildings, this paper studies its impact - through changes in rain, wind and other climatic parameters - on the amount of water which penetrates the outmost layer of ventilated facades. Importance of three uncertainty factors of the climate data are investigated: uncertainties from global climate models, emissions scenarios and spatial resolutions. Consistency of the results is examined by modelling walls with different materials and sizes, as well as using two mathematical approaches for WDR modelling. Sensitivity of the wall simulations to the wind data is assessed by using synthetic climate with sole wind data. According to the results, higher amounts of moisture will accumulate in walls in the future; climate uncertainties can cause variations up to 13% in the calculated 30-year average of water content and 28% in its standard deviation. Using sole wind data can augment uncertainties with up to 10% in WDR calculations, however it is possible to neglect changes in future wind data.