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. (1st June 2019)

• Record Type: Journal Article
• Authors: Zheng, Yuanfan; Weng, Qihao
• Is Part Of: Energy. Volume 176(2019)
• Page Start: 641
• Page End: 655
• Publication Date: 2019-06-01
• Subjects: Buildings -- Energy demand -- Climate change -- High resolution modeling -- Urban sustainability -- Los Angeles; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.04.052
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: 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. Highlights: GIS approach with climate modeling, building energy simulation, and inventory. Most buildings showed an apparent increase of energy demands under A1F1 and A2. Energy demand for all buildings increased April to October, peaked in August. Cooling energy demand showed great change between current and future climate. Areas with dense commercial buildings predicted largest increase in energy use.