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

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 regard...

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Detalles Bibliográficos
Autores principales: Ngah Nasaruddin, Afiqah, Tee, Boon Tuan, Mohd Tahir, Musthafah, Md Jasman, Md Eirfan Safwan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881228/
https://www.ncbi.nlm.nih.gov/pubmed/33614870
http://dx.doi.org/10.1016/j.dib.2021.106797
Descripción
Sumario: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.