Climate Change Energy Data Simulation Scenarios: Urban Thermal Environment Improvement through Demand Control Strategy

Due to various factors such as urban construction development and excessive energy usage, the urban heat island effect causes the air temperature in urban centers to be higher than in surrounding suburban areas. The causes of the urban heat island effect are numerous, including the thermal environmental impact caused by heat emissions from air conditioning energy, which is a concern for countries around the world. This study focuses on the built-up areas of Taipei City and New Taipei City in Taiwan and proposes solutions based on previous research literature. The approach involves using machine learning methods to identify high-energy usage areas caused by air conditioning and their corresponding temperature differences to formulate strategies for mitigation.

The study uses the National Science and Technology Center for Disaster Reduction and the High Resolution Atmospheric Model (HiRAM) to conduct global climate simulation under the RCP8.5 warming scenario. Through dynamic downscaling using WRF, the data was scaled down to a 5km resolution for the Taiwan region. The climate data produced was then used in Energy Plus to simulate residential air conditioning cooling power consumption and estimate Energy Usage Intensity (EUI). Additionally, cooling demand was controlled in areas with higher EUI, and a neural network prediction model was utilized to estimate air conditioning cooling demand in various heat zones.

The study aims to implement control strategies using the public sector's Building Energy Management System, such as controlling air conditioning usage during peak periods through load shedding to keep air temperature rise within a set threshold. This approach not only reduces energy consumption but also effectively reduces air conditioning heat emissions, thereby enhancing thermal comfort in urban environments. Based on the research framework, the results show that under the HiRAM scenario of a 2°C temperature rise, building air conditioning EUI is expected to increase by 15-20%. The accumulated air conditioning heat emissions become one of the major factors in air temperature rise. Furthermore, simulation verification using neural networks and the implementation of energy strategies demonstrate that effective control of EUI thresholds can lower regional air temperatures by approximately 0.2 to 0.5°C.

 

Keywords: Urban heat island, High resolution atmospheric model, Energy plus, Demand control, Machine learning