• Energy Research

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.

Co-optimization of urban morphology and distributed energy systems is key to curb energy consumption and optimally exploit renewable energy in cities. Currently available optimization techniques focus on either buildings or energy systems, mostly neglecting the impact of their interactions, which limits the renewable energy integration and robustness of the energy infrastructure; particularly in extreme weather conditions. To move beyond the current state-of-the-art, this study proposes a novel methodology to optimize urban energy systems as interconnected urban infrastructures affected by urban morphology. A set of urban morphologies representing twenty distinct neighborhoods is generated based on fifteen influencing parameters. The energy performance of each urban morphology is assessed and optimized for typical and extreme warm and cold weather datasets in three time periods from 2010 to 2039, 2040 to 2069, and 2070 to 2099 for Athens, Greece. Pareto optimization is conducted to generate an optimal energy system and urban morphology. The results show that a thus optimized urban morphology can reduce the levelized cost for energy infrastructure by up to 30%. The study reveals further that the current building form and urban density of the modelled neighborhoods will lead to an increase in the energy demand by 10% and 27% respectively. Furthermore, extreme climate conditions will increase energy demand by 20%, which will lead to an increment in the levelized cost of energy infrastructure by 40%. Finally, it is shown that co-optimization of both urban morphology and energy system will guarantee climate resilience of urban energy systems with a minimum investment.

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.

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.

Abstract Climate change and increased urban population are two major concerns for society. Moving towards more sustainable energy solutions in the urban context by integrating renewable energy technologies supports decarbonizing the energy sector and climate change mitigation. A successful transition also needs adequate consideration of climate change including extreme events to ensure the reliable performance of energy systems in the long run. This review provides an overview of and insight into the progress achieved in the energy sector to adapt to climate change, focusing on the climate resilience of urban energy systems. The state-of-the-art methodology to assess impacts of climate change including extreme events and uncertainties on the design and performance of energy systems is described and discussed. Climate resilience is an emerging concept that is increasingly used to represent the durability and stable performance of energy systems against extreme climate events. However, it has not yet been adequately explored and widely used, as its definition has not been clearly articulated and assessment is mostly based on qualitative aspects. This study reveals that a major limitation in the state-of-the-art is the inadequacy of climate change adaptation approaches in designing and preparing urban energy systems to satisfactorily address plausible extreme climate events. Furthermore, the complexity of the climate and energy models and the mismatch between their temporal and spatial resolutions are the major limitations in linking these models. Therefore, few studies have focused on the design and operation of urban energy infrastructure in terms of climate resilience. Considering the occurrence of extreme climate events and increasing demand for implementing climate adaptation strategies, the study highlights the importance of improving energy system models to consider future climate variations including extreme events to identify climate resilient energy transition pathways.

An integrated approach is presented in this study to design electrical hubs combining optimization, multi-criterion assessment and decision making. Levelized Energy Cost (LEC), Initial Capital Cost (ICC), Grid Integration Level (GI), Levelized CO2 emission (LCO2), utilization of renewable energy, flexibility of the system, loss of load probability (LOLP) are considered as criteria used to assess the design. The novel approach consists of several steps. Pareto analysis is conducted initially using 2D Pareto fronts to reduce the dimensions of the optimization problem. Subsequently, Pareto multi objective optimization is conducted considering LEC, GI and ICC which were identified as the best set of objective functions to represent the design requirements. Next, fuzzy TOPSIS and level diagrams are used for multi-criterion decision making (MCDM) considering the set of criteria and the boundary matrix that represents the design requirements of the application. Pareto analysis shows that 5D optimization problem can be reduced to a 3D optimization problem when considering LEC, ICC and GI as the objective functions. Finally, results obtained from the case study shows that the novel method can be used design distributed energy systems considering a set of criteria which is beyond the reach of Pareto optimization with different priority levels.

AbstractSince the beginning of the 21st century, straw-bale buildings are reappearing in the world; however, their thermal performances were not thoroughly investigated up to now. The purpose of this study is to analyze thermal behavior and energy performance of a straw-bale building in Switzerland. Using Pleiades+Comfie Software, building designs have been studied to understand the best way to mitigate overheating risks due to the low heat capacity of straw. Thermal-dynamic results and Life Cycle Assessment conclude that straw bale buildings can be a sustainable alternative in the energy evolution of building construction, due to its low embodied energy and excellent thermal performance.