Modeling the effect of climate change on building energy demand in Los Angeles county by using a GIS-based high spatial- and temporal-resolution approach
| Autor: | Qihao Weng, Yuanfan Zheng |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Energy demand
020209 energy Mechanical Engineering Climate change Building energy 02 engineering and technology Building and Construction Absolute difference Pollution Industrial and Manufacturing Engineering Current (stream) General Energy 020401 chemical engineering Climatology Temporal resolution 0202 electrical engineering electronic engineering information engineering Environmental science Climate model 0204 chemical engineering Electrical and Electronic Engineering Building energy simulation Civil and Structural Engineering |
| Zdroj: | Energy. 176:641-655 |
| ISSN: | 0360-5442 |
| DOI: | 10.1016/j.energy.2019.04.052 |
| Popis: | Climate change affects the demands for heating and cooling in buildings. This study proposed a GIS-based approach to combine climate modeling, building energy simulation, and inventory of building characteristics to quantify climate change's effect on building energy demand in Los Angeles, California. The impact was assessed by comparing building energy demands under current and future climate conditions through two metrics: relative change (RC) and absolute difference (AD), in annual, monthly, and diurnal scales under A1F1 and A2 emission scenarios. A spatial analysis was performed to assess neighborhoods vulnerable to climate change. Results suggest that most building types showed an apparent increase of energy demands under both scenarios. The increase of cooling energy demand resulted in great changes in RC and AD. Larger changes were observed at finer time scales. The energy demand for buildings increased from April to October, but decreased from November to March. The largest positive AD of total energy for all building types occurred in August, ranging 1.8–30.9 MJ/sqm, but the characteristics of diurnal AD varied with building types. Areas with dense tall commercial buildings would foresee the largest increase in energy demand. Our approach can foresee the sensitivity of building energy demands at different spatio-temporal scales. |
| Databáze: | OpenAIRE |
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