Modeling Steady State Power Consumption in Split Air Condition Units Based on Indoor Environmental Condition using Cumulative Sum and Machine Learning Techniques
Description
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.
Files
Steps to reproduce
General Notes : A. Each power and indoor environmental quality data are saved on "MatchedData_IAQ" files coded with their corresponding room numbers [e.g., MatchedDarta_IAQ (808-1)]. Classrooms are assigned to 809-1, 809-2, and 809-3; Laboratory Rooms to 801-1, 801-2, and 801-3; Computer Rooms to 802-1, 802-2, and 808-3; shared office space to 808-1; and, solo office space to 808-2 and 808-3. B. The "StabilizationResults" file contains the following simulation results: (a) Detected changes in temperature points, (b) Detected changes in power points, (c) Stabilization or steady-state information, (d) highest stabilization or steady-state values for each day, (e) Daily Steady State and Indoor Environmental Values, (f) Figures simulation results of a to e, (g) Correlation analysis of daily power vs indoor environmental and air quality, (h) Correlation analysis of peak power vs indoor environmental and air quality, (i) Steady-state power prediction using machine learning classification algorithms. Processes involved in deriving the simulation results: 1. Power, environmental air quality and indoor air quality data are cleansed and mapped to ensure that their entries correspond exactly to their date and time of recording. Matlab function used : "match_and_map_and_save_data.m". 2. Change points in temperature and power were identified using cumulative sum technique to define the peak and change of state condition of the ACU power consumption. Cumulative sum values typically remain flat or resets to zero until significant cumulative deviations accumulate to exceed the threshold, at which point a reset occurs, marking a change point. Matlab function used : "detectchange_CUSUM.m". 3. Steady state power conditions were calculated using the cumulative sum simulation output data. Matlab function used : "computeStabilizationDurations.m". 4. Matlab Classification Learner toolbox were executed and applied to the resulting values of the steady state power values. The classifier was used to predict the steady state power values using indoor environmental and air quality conditions as predictors. In the process of steady-state power estimation of air conditioning units, machine learning models are trained with a dataset including variables such as date, operating hours, duration of steady state, indoor temperature after settling, daily power consumption, indoor humidity, heat index, CO and CO2 levels, and outdoor heat index. The procedure involves data preparation, launching the Classification Learner, feature selection, and model choice up to training, evaluation, and exporting the model— thereby complete classification of ACU power consumption by different machine learning techniques. A predictive model is built that can estimate the power consumed by ACUs not in the database or not met before, therefore helping an energy efficiency strategy be optimized.