## 2 Materials and Methods
### ACU Electrical Power Consumption and Environmental Air Quality Monitoring System  
Energy consumption monitoring was performed using an Arduino Mega 2560 and Raspberry Pi 3B+ interconnected via LAN. The current signal converter modules used an SCT-013-000 non-invasive AC clamp sensor and a current rating of 100 A:50 mA as measured using an analog AC current sensor. Si7021 sensors in the indoor air quality (IAQ) monitoring systems were used to measure temperature (\(\pm\)0.4 accuracy) and humidity (\(\pm\)3 accuracy), while CCS811 sensors measured CO\({}_{2}\) levels. Pre-set locations were chosen for IAQ monitoring, as the outdoor environmental conditions (temperature, humidity, and CO\({}_{2}\) levels) fluctuate less than indoor conditions. The wet bulb temperature value was computed using Equation (1), based on the recorded indoor dry bulb temperature and relative humidity [50]. \[ \begin{split} T_{w}=T\ tan^{-1}[& 0.151977(RH+8.313659)^{\frac{1}{2}}]+tan^{-1}(T+RH)-tan^{-1}(RH-1.676331)+\\ & 0.00391838\ (RH)^{\frac{3}{2}}\ tan^{-1}(0.023101 RH)-4.686035\end{split}  \tag{1}\]  
where \(T_{W}\) is the indoor wet bulb temperature (in \({}^{\circ}\)C), \(T\) is the indoor dry bulb temperature (in \({}^{\circ}\)C), and \(RH\) is the indoor relative humidity (in %). To ascertain the accuracy of the measured power values, the Sanwa DCM400 Clamp meter (Tokyo, Japan) was used as a point of reference. The average discrepancy between testing samples using the SCT-013-000 clamp sensor and the Sanwa DCM400 was employed for the determination of factors of bias when calculating the measured power. The same method was employed for the calibration of air quality and environmental parameter sensors. For dry bulb temperature, this was against the readings of a Benetech Air Flow Anemometer GM8902+ (Shenzhen, China). For both temperature and relative humidity, aDienmern DM601B Air Quality Detector and Multifunctional Smart Monitor (Guangdong, China) was used, and a Henan Zhongan S318 portable multi-point detector (Zhengzhou, China) was used for air quality measurements. For web-based database management, the Raspberry Pi 4 Model B was installed with the Debian 12 Bookworm Kernel 6.6 supported by PhpMyAdmin 5.2.1. A LAMP (Linux, Apache, MySQL, and PHP) server was developed in the Raspberry Pi in order to save the memory when developing webpages for tracking and analyzing the power consumption every five minutes. A similar work on annual power consumption has been conducted in a university based on hourly and quarter-hourly power data [51]. Programs with dynamic webs were served from PHP--a server-side language--while the MATLAB R2024a software was used for data analysis and processing. Each room was installed with a Mitsubishi split-type inverter ACU (3-in-1 or 5-in-1) (Tokyo, Japan) with power ratings of 1.5 up to 6.0 horsepower. Data on ACU power consumption, indoor temperature, humidity, and CO\({}_{2}\) levels (measured every 5 min) were collected for 6 months. The average outdoor temperature during the period of logging was 29.40 \({}^{\circ}\)C and the relative humidity was 67% [52]. The sampling frequency of the collected data was also higher than that in [53], where 96 sampling points were sampled every day. The ACU power consumption was measured for instructional rooms such as lecture halls, laboratory rooms, and computer rooms--all with a seating capacity of 36--as well as office spaces with a maximum capacity of 15 persons and cubic rooms with solo occupancy. The typical usage time of instructional classrooms is 2 to 8 h on weekdays, while office areas are usually used for between 8 and 10 h. Three major datasets were used in this study: (1) real-time ACU power consumption; (2) indoor environmental and air quality values; and (3) instructional room occupancy for 1 academic year (AY), 2023-2024. An academic year is composed of 3 trimesters, each with a typical period of 13.5 weeks.