Impact of bin weather data and climate change on air conditioning in the tropics / Wong Chun Mun

• Main Author: Wong , Chun Mun
• Format: Thesis
• Published: 2024
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://studentsrepo.um.edu.my/15399/2/Wong_Chun_Mun.pdf; http://studentsrepo.um.edu.my/15399/1/Wong_Chun_Mun.pdf; http://studentsrepo.um.edu.my/15399/

This study addressed several research gaps in academic field of bin weather data due to a lack of conducted studies that comprehensively explores the impact of bin weather data and climate change on air conditioning in tropics. Bin weather data are generated for four cities in Malaysia: Bayan Lepas, Kuala Terengganu, Kuching, and Senai, spanning from 2001 to 2018. Bin method simulations are computed, indicating deviations of under 1.6% when utilising the mean temperature of occupied period instead of the bin weather data. However, the application of annual mean outdoor temperature yields a larger deviation, ranging from 11.55% to 16.68%. Results show that mean temperature of occupied period is a viable alternative to bin weather data. Experimental works are conducted to investigate the energy consumption of HVAC system while operating under both the Energy Recovery Ventilation (ERV) and conventional ventilation systems. ERV system in heat exchange mode consume 32.64% less daily A/C energy per ΔT and 27.04% less daily total HVAC system energy per ΔT than exhaust fans, at a similar ventilation rate. Based on the parameters of experiments conducted, energy consumption is simulated with bin method. The percentage error between calculated energy consumption and actual energy consumption varies from 3.89% to 30.55%. Factors that can influence the performance of bin method include differences between the set temperature and the actual indoor temperature, insensitivity of air conditioner temperature sensors, application of non-identical operating characteristics, variations in air conditioner loads, CLTD/SCL/CLF method limitations, granularity of bin temperature basket, and energy recovery function for the ERV system. This research also studied the climate change impacts on bin weather data and determine the recommended lifespan of bin weather data for the selected cities. In Bayan Lepas, the percentage difference of bin weather data for the years 2001 to 2015 ranges from -1.93% to 8.08%, showing no apparent trend of climatic change. Bin weather data of Kuala Terengganu and Senai has a recommended lifespan of 2 years based on a data variation threshold of 5%. However, if higher threshold of 10% is applied, the recommended lifespan for Kuala Terengganu and Senai could be extended to 4 and 8 years respectively. Kuching has a recommended lifespan of at least 15 years for bin weather data due to its lack of apparent climatic change. A total of 61 simulation cases are conducted to analyse the climate change impacts on bin method. Although simulated cases for Bayan Lepas did not show any consistency, the deviation of total energy consumption for other cities ranges from -1.36% to -4.69% in Kuala Terengganu, from -0.74% to -4.61% in Kuching, and from -2.24% to -7.35% in Senai. This research has successfully developed updated bin weather data for cities located in tropics, identified the significance of bin weather data and compatibility of mean temperatures, evaluated the performance of bin method simulations, and investigated the climate change impacts on bin weather.