Correlating cooling energy use with urban microclimate data for projecting future peak cooling energy demands: Residential neighbourhoods in Seoul

The paper presents a relational study of correlating cooling energy use with local weather station and apartment price data in Seoul. The overall analysis at a macro-level shows monthly variations in the correlation coefficients of cooling energy use and local weather station data during summer months. A further analysis at a micro-level shows temporal and spatial variations in the correlation. As the August correlation appears the strongest across all city districts, up to r=0.972, a simple bivariate regression (SBR) model is derived to predict peak cooling energy use for each district. Given the latest climate change projections for Seoul, we use the SBR models to estimate increases of cooling energy use for each city district in August 2050s. The largest predicted increase rate (IR) is 96.1% in one city district (from 124.5% in 2012-220.6% in 2047). The smallest IR is 6.0% in another city district (from 51.5% to 57.5%). In 2047, the city district with the highest predicted IR is up to 292.8%, while the lowest is up to 57.5%. We discuss the implications of the projected future peak cooling energy demands for sustainable resilience as well as citizen's health and wellbeing.