Modeling Steady State Power Consumption in Split Air Condition Units Based on Indoor Environmental Condition using Cumulative Sum and Machine Learning Techniques

An energy consumption monitoring system logs, records, and measures building power consumption. Real-time measurement covers room temperature, humidity, CO2 gas sensors, circuit breaker power con-sumption. The data on electricity power usage, indoor temperature, and air quality are collected and systematically analyzed and evaluated. These systems are very necessary for the real-time measurement of power usage, indoor temperature, humidity, and CO2. Remote power consumption sub-metering modules are based on Arduino Mega 2560 coupled with a Raspberry Pi 3B+ across a LAN. Each room is installed with a Mitsubishi split-type inverter ACU, with power rating ranges from 1.5 up to 6.0 horsepower, depending on the room type and size. For 11 weeks, the consumption of ACU power, inside temperature, humidity, and CO2 levels were measured every 5 minutes on months with average outdoor temperature was 29.40°C and relative humidity was 67%. The data used for curve fitting and analysis to connect and model power consumption with indoor temperature and air quality conditions are collected only during classes or office operations. Selected were five rooms of mixed use, from instructional rooms down to the 36-seater lecture hall, laboratory room, computer room, to office spaces with a maximum capacity of fifteen (15) persons, and cubicle room built for solitary usage. The ideal operating time of instructional classrooms should be at least two (2) to eight (8) hours a day, while office areas are usually occupied from eight (8) to ten (10) hours. The objective of the present work focuses on the steady-state power consumption of ACUs. Furthermore, to : (1) compare empirical models with those based on ACU power consumption and factors which influence its steady-state time; (2) demonstrate the ability of AI techniques to classify power consumption; and (3) derivation of potential power savings from changing ACU set points. It is the goal of this study to help with the generation of more general and robust models on optimizing the ACU performance, which in turn raises occupant comfort and energy efficiency. Cumulative sum technique (CUSUM) was used to calculate sum of deviations from a target value to detect significant changes in mean value of power and temperature, adjusted by a standard deviation-based allowance. The resulting values can be found on Table 1 showing that higher ACU setpoints (i.e., warmer temperatures) give lower levels of daily and steady-state power use. The MATLAB Classification Learner application is used to develop predictive models based on supervised learning algorithms. Table 3 summarize the efficacy of different machine learning strategies in predicting energy use by ACUs, offering valuable insight into the choice of the models that one can use, given the complexity and nature of the data.