Cities will become substantially warmer in the future due to the global rise in temperature, leading to low indoor air quality and adverse impacts on human health. In addition, buildings are considered at risk of overheating since the current building design will become irrelevant or extremely inefficient. Consequently, the rise of global temperatures will lead to the redefinition of the energy systems to use, as a decrease in heating demand and an increase in cooling consumption are expected. Besides, as cooling demand depends on electric-based energy systems, their use increases greenhouse gas emissions in countries with fossil fuel-based energy grid mix.
Considering the long lifespan of buildings and the fact that new buildings are still the primary type of construction worldwide, it is urgent to (i) realize that current building design guidelines are not adequate for future climate, (ii) determine the most appropriate design recommendations for each of the future climate scenarios, and (iii) train building design professionals to face the adverse aspects of climate change.
The CLING project will fill the gap with four significant contributions. First, the project team selects future climate scenarios and time horizons of the Intergovernmental Panel on Climate Change (IPCC) scenarios. Then, the coarse resolution of the climate model predictions is downscaled to the fine spatial and temporal resolutions required for building thermal simulations. For that purpose, a to-be-developed new ‘morphing’ technique will match past weather data to the predicted climatic variables.
The dynamic simulation will use the ‘morphed’ weather data to assess buildings’ thermal and energy performance generated by a radically new performance-based generative design approach. The team will develop this approach to produce different building types in a statistically significant number, capitalizing on the deep learning field’s latest advances. In addition, a novel surrogate method will be developed based on an ensemble of neural networks to accelerate estimating complex and large buildings’ performance. The generated datasets are then analyzed using a developed-specific statistical model to quantify the climate projections and operation uncertainties. Lastly, the interrelations between geometry, construction, and energy systems variables are determined for each climate change scenario in different regions worldwide.
Future buildings’ design guidelines are realized as design paths from the results. These will include the influence of each design action, the mitigation and adaptation strategies, and the decision variables’ interrelated roles. The buildings’ resilience to extreme weather is also studied. Both the most and the least recommended design actions are identified. In addition, outreach and training initiatives are organized on climate change-based design to make those guidelines available to professionals and students.
Understanding how future buildings must be designed has global importance due to the enormity of environmental problems related to climate change. If successful, our proposed research has the potential to accelerate the implementation of policies that prevent the building stock from being locked into suboptimal performances that might compromise the nations’ ability to reach their carbon emissions targets.
The project is a co-promotion partnership between ADAI (leader), CMUC, CESAM, and CONSTRUCT. The team covers critical knowledge areas, such as mechanical and environmental engineering (ADAI), meteorology and climatology (CESAM), architecture and civil Engineering (CONSTRUCT), and mathematics (CMUC). The proposed research combines different investigators’ expertise to synthesize new building design guidelines in a comprehensive study on the built environment performance, such as computational design methods, numerical weather forecasts, numerical simulation, indoor air quality, energy efficiency in buildings, building design, construction, and applied mathematics.
The research outcomes will ultimately support policymaking on a national level and point out directions to develop new tools and building technologies. Lastly, the project aligns with two of the 2030 Agenda from United Nations goals (SDGs 13 and 11).