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=.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 to 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.
Abstract-A climate change simulation framework for intelligent green building adaptation design is proposed. The simulation framework is developed for studying environmental performance of existing or proposed green buildings under present and future urban microclimate conditions. It draws on a synthesis of environmental computer simulation in three areas: (1) overall climate change scenario modelling at city level, (2) outdoor urban microclimate modelling at neighbourhood level, and (3) indoor environmental simulation at building level. A case study of applying the climate change simulation framework to an existing university campus green building is presented for 2012 and 2050. In response to the simulation results, strategies for adapting the case study green building in relation to its changing urban neighbourhood are assessed as an example. The case study shows that the simulation framework can generate requirements for intelligent green building adaptation design by linking urban microclimate change projection to simulated energy demand in maintaining building indoor thermal comfort.
As hot days are getting hotter and more frequent, urban dwelling is expected to increase cooling energy use in current and future climate. The applicability of dynamic building simulation in estimating cooling loads of a city's housing stock can be limited due to lack of fine-grained on-site current and future weather inputs. For predicative modelling of residential cooling energy demand to aid a city's energy supply planning resilient to excessive heat conditions, it requires cooling energy demand projection based on a relational account of (1) the thermal-environmental interaction between housing stocks and urban microclimate conditions, (2) the city dwellers' cooling energy use behaviour, and (3) the city's climate projections. In this paper, we introduce an 'archetype-in-neighbourhood' framework to meet these requirements. Combining empirical urban data modelling and EngeryPlus model calibration, this framework was developed to obtain statistically a maximal cooling energy demand model of a city's housing stock during yearly hottest periods. We applied the framework to multiple datasets selected from Seoul's open urban data sources for the period of 2014-2017 (2014 being the earliest year of data availability, 2017 being the end of the study period), including metered electricity use data of 659 apartment buildings (51,351 households) sampled from 18 city districts. The results show that maximal month cooling energy demand (MMCD, kWh/m 2) of Seoul's housing stock can be expressed as a regression function of two determinants: (1) the city's average outdoor temperature during the hottest month period (Tex, C), and (2) estimated indoor cooling temperature set-point (Tin, C) of the city' housing stock during the same period. Through a k-fold (k=4) validation, the current regression model (2014-17) was evaluated to have an overall coefficient of determination R 2 =.969. Assuming no housing stock renovation, we applied the model to generate scenarios of maximal month cooling demand in future years according to some of the highest summer temperatures projected for Seoul (RCP8.5 2045, RCP4.5 2047, MM5 2071-2100). We conclude this paper with a brief discussion of the implication for cooling energy supply planning and further work to extend the applicability of this new framework to housing stock adaptation planning and design.
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