Multi-scale and dynamic energy mapping for strategic decision making and integrated energy management in Wallonia

In the context of smart cities, this doctoral thesis addresses the energy challenge linked to existing building stocks, by proposing methods and tools for estimating and analysing their energy consumption on the territorial scale, in combination with multi-scale and dynamic energy mapping. The methodologies and tools developed are applied to the entire stock of buildings in Wallonia (Belgium), which includes more than 1.7 million buildings. The results should help implement smart energy management in large building stocks. Firstly, the annual heat consumption (HC), heat demand (HD), and electricity consumption (EC) of the regional building stock are assessed, statistically analysed and mapped on different scales. Based on mean values at the neighbourhood scale, the HD is lower than the HC of 16.44%, 15.78% and 9.26% for the residential, tertiary industrial buildings respectively. Statistical analysis tests were performed to analyse to what extent different types of variables explain the annual EC. Moreover, the impact of climate change on the existing building stock's HC and cooling EC evolution until 2050 is performed using artificial intelligence models. The HC reduction of the entire building stock until 2050, calculated at the regional scale, reaches -8.82 % for residential, -10.00% for tertiary, and -11.26% for industrial buildings. The projected increase in EC for cooling in existing tertiary buildings is + 11.94% in 2050. Further, the land use mix (LUM) of residential, tertiary and industrial buildings on a statistical sector scale is assessed based on entropy (E) and Herfindahl-Hirschman Index (HHI). On the 12 generated LUM classes, 3 prospective scenarios based on climate change, buildings renovation rate, and demography are applied. Energy consumption reduction tendencies are different in classes. Finally, the dynamic hourly HC and EC profiles per m² of different building archetypes are modelled, using sigmoid functions and programming in Python, based on previously assessed annual HC and EC and the temperature data. The simulated dynamic hourly profiles of HC and EC of 4 building archetypes are calibrated and validated using monitoring data and indices proposed by ASHRAE.Wal-e-Citie