Abstract
Considering climate change and energy resource depletion under rapid urbanization trends in the urban environment, the relation between land-use, climate change, and urban energy demand is gaining attention. However, a limited number of studies are focusing on the effect of microclimate change, and more specifically, temperature change on energy demand at an urban scale. This study includes empirical spatial and temporal modeling to identify how urban morphology indicators (UMIs), land surface temperature (LST), and neighboring land-use compositions affect urban energy demand using an extensive data set for the case study of Eindhoven, the Netherlands. For this purpose, the ordinary least square regression (OLS) and geographically weighted regression (GWR) models are employed. The results show, there is a significant spatial relation between UMIs, neighboring land-use compositions, and urban energy demand. Furthermore, the impact of dwelling types on urban energy demand is discussed. The results can be applied to sustainable urban planning targeting energy reduction, climate adaptation, and help local authorities for implementing energy management strategies.
Highlights
Please check the document version of this publication: A submitted manuscript is the version of the article upon submission and before peer-review
urban morphology indicators (UMIs) and neighboring land-use together can best explain the energy demand model f Neighboring land-use composition is important to energy demand modeling in a o city o Gas demand is more influenced by UMIs and N450 compared with electricity r demand -p The higher buildings density configurations lead to greater heat-energy efficiency l Pre Abstract na Considering climate change and energy resource depletion under rapid urbanization trends r in the urban environment, the relation between land-use, climate change, and urban energy u demand is gaining attention
This study includes empirical spatial and temporal modeling to identify how urban morphology indicators (UMIs), land surface temperature (LST), and neighboring land-use compositions affect urban energy demand using an extensive data set for the case study of Eindhoven, the Netherlands
Summary
This study includes empirical spatial and temporal modeling to identify how urban morphology indicators (UMIs), land surface temperature (LST), and neighboring land-use compositions affect urban energy demand using an extensive data set for the case study of Eindhoven, the Netherlands. For this purpose, the ordinary least square regression (OLS) and geographically weighted regression (GWR) models are employed. The details of the study area are presented, followed by a data aggregation method to construct an urban energy demand model, and statistical information about the model included land-use functions and dwelling types that are used in the analyses.
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