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.

Climate change presents threatening challenges to the built environment. Global warming is making traditional design practice inadequate. For example, in mild climates, as building design follows an overall passive solar heating strategy complemented with night ventilation, most studies focused on reducing the heating losses during winter. Therefore, highly insulated envelopes were proposed in Madrid, Milan and Lisbon as climate mitigation measures. These results were expected, as buildings in these regions are known for their low indoor air quality due to being poorly built. Thus, the benefits of high insulation levels were expected.

However, these studies implicitly assume that occupants will just adapt to the future climate without resorting to cooling-based air-conditioning systems. However, this assumption is questioned by recent studies that argue that occupants will purchase those systems, thus shifting from today's winter heating paradigm towards cooling-based needs. As future climate scenarios indicate longer hours of solar availability, the electricity consumed by cooling systems may be compensated using photovoltaic and thermal collector systems. However, these systems' implementation viability depends strongly on surroundings, building shape, and available installation surface area.

It is, therefore, essential to analyze the building's performance holistically and on an annual basis. Whenever incorporated in the analysis, studies show that cooling needs are a significant part of the total energy consumed. Thus, highly insulated buildings will increase overheating, which was estimated for several countries. For example, retirement villages in the UK, designed to meet the net-zero energy target, expose the elderly to grave health risks from overheating. In Cyprus, the country's annual electricity demand will rise by 6 %. In Switzerland, apartment buildings' cooling demand will increase to 244 %. Similar concerns on overheating were also found in Belgium.

The aims of the research are:

1. to characterize building types and their operation according to IPCC energy use and greenhouse gas emission scenarios
2. to select future weather from climate models from the IPCC latest report
3. to develop a new weather morphing technique
4. to develop a radically new space allocation generative design algorithm to produce alternative buildings
5. to develop a novel ensemble neural network as a surrogate method to dynamic simulation
6. to develop a new statistical model that quantifies the uncertainties associated with climate predictions and building operation
7. to generate future weather and buildings datasets using the developed algorithms
8. to analyze the datasets using the developed statistical model
9. to realize new building guidelines from the analysis results of the dataset

The project team will reach these aims in five work packages (WP). Aims (1) and (2) are met in the first work package, aims (3), (4), and (5) are concluded in the second, aims (6), (7), and (8) are completed in the third, and lastly, aim (9) is met on the fourth work package. The remaining work package is dedicated to disseminating research findings and the management of the project.

Climate change, climate prediction, statistical model, datasets