Using urban building energy modeling to quantify the energy performance of residential buildings under climate change.

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization. Urban building energy modeling (UBEM) is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations, supporting the implementation of carbon emission reduction policies. Currently, most studies focus on the energy performance of archetype buildings under climate change, which is hard to obtain refined results for individual buildings when scaling up to an urban area. Therefore, this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas, by taking two urban neighborhoods comprising 483 buildings in Geneva, Switzerland as case studies. In this regard, GIS datasets and Swiss building norms were collected to develop an archetype library. The building heating energy consumption was calculated by the UBEM tool-AutoBPS, which was then calibrated against annual metered data. A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%. The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results showed a decrease of 22%-31% and 21%-29% for heating energy consumption, an increase of 113%-173% and 95%-144% for cooling energy consumption in the two neighborhoods by 2050. The average annual heating intensity dropped from 81 kWh/m2 in the current typical climate to 57 kWh/m2 in the SSP5-8.5, while the cooling intensity rose from 12 kWh/m2 to 32 kWh/m2. The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7% and 18.6%, respectively, in the SSP scenarios. The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.

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 climate resilient urban energy systems: a review.

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.