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A study on temperature-setting behavior for room air conditioners based on big data
مطالعه رفتار تنظیم دما برای سیستم های تهویه مطبوع اتاق بر اساس داده های بزرگ-2020 The set-point temperature of room air conditioners (RACs) is extremely critical for cooling
energy consumption of residential buildings. However, current research on temperature-setting
behavior is limited owing to the limitations of data acquisition. This study aims to identify the
typical temperature-setting patterns for RACs and explore the association of temperature-setting
behavior with other RAC operation characteristics. The data obtained from the big data cloud
platform of an RAC manufacturer were analyzed in this study. These data consist of measured
data from 966 bedroom RACs (BRACs) and 321 living room RACs (LRACs). First, the RAC
operation characteristics, involving five parameters, namely, set-point temperature, set wind speed,
indoor temperature, operation duration, and energy consumption, were extracted from the raw data
by transforming, aggregating, and merging the bottom-level measured data. Subsequently, cluster
analysis was performed to identify various and typical temperature-setting behavior patterns. Five
typical temperature-setting patterns for BRACs and six typical patterns for LRACs were obtained.
Afterwards, data mining methods of difference analysis and association analysis were employed to explore the differences and association, respectively, of different temperature-setting patterns with
other operation characteristics of RACs (e.g., set wind speed, indoor air temperature, operation
duration, and energy consumption). The results of this study can provide researchers with
references of temperature-setting strategies in residential building energy simulation and quantify
the energy impacts of diverse temperature-setting patterns in residential buildings. Keywords: Occupant behavior | Room air conditioner | Set-point temperature | Data mining | Cluster analysis |
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