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Data Assessment on the relationship between typical weather data and electricity consumption of academic building in Melaka

机译:Melaka学术建筑典型天气数据与电力消耗的数据评估

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摘要

Exposure to hot and humid weather conditions will often lead to consuming a vast amount of electricity for cooling. Heating, ventilation, and air conditioning (HVAC) systems are customarily known as the largest consumers of energy in institutions and other facilities which raises the question regarding the impact of the weather conditions to the amount energy consumed. The academic building is a perfect example where a constant fixed daily operating characteristic is measured by the hour, aside from the occasional semester break. Therefore, it can be assumed that the daily HVAC services on an academic facility will operate on a fixed schedule each day, having a similar pattern all year round. This article aims to present an analysis on the relationship between typical weather data by implying the test reference year (TRY) and academic building electricity consumption in an academic building located at Durian Tunggal, Melaka. Typical weather data were generated in representing the weather data between 2010 and 2018 using the Finkelstein–Schafer statistic (F-S statistic) in addition to a data set of electricity consumption. Descriptive analysis and correlation matrix analysis were conducted using JASP software for two sets of sample data; Set A and Set B, with data points of 12 and 108, respectively. The result showed an alternate result with a positive correlation between 1)mean temperature-electricity consumption, and 2)mean rainfall-electricity consumption for data Set A, and a negative correlation between 1)mean temperature-electricity consumption and 2)mean rainfall-electricity consumption for data Set B.
机译:暴露于热和潮湿的天气条件通常会导致消耗大量的冷却电力。加热,通风和空调(HVAC)系统通常被称为最大的机构中能源消费者和其他设施中的最大消费者,这提出了关于天气条件的影响到消耗的能量的问题的问题。学术建筑是一个完美的例子,持续固定的日常操作特征是一小时,除了偶尔学期的休息。因此,可以假设学术设施的每日HVAC服务每天将在固定的时间表上运行,全年具有类似的模式。本文旨在通过暗示在位于榴莲乔格纳,Melaka的学术建筑中的测试参考年份(尝试)和学术建筑电力消耗来分析典型天气数据之间的关系。除了数据集的电力消耗之外,还产生了在2010年和2018之间的天气数据表示典型的天气数据。使用JASP软件进行两组样本数据进行描述性分析和相关矩阵分析;设置A和SET B,分别具有12和108的数据点。结果显示了1)平均电力消耗的正相关性的替代结果,2)平均降雨 - 电力消耗用于数据集A,1)平均电力消耗与1)的负相关性和2)平均降雨 - 数据集电力消耗B.

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