Analysis of Power Consumption and Maximum Demand on Power Factor Using Support Vector Machine Regression

• Published in: 14th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2024 - Proceedings
• Main Author: Isa S.N.M.; Daud K.; Ibrahim M.N.; Samat A.A.A.; Omar S.; Shafie M.A.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207060668&doi=10.1109%2fICCSCE61582.2024.10696146&partnerID=40&md5=25f71fa7a167d4d1114ea6969455b289

This paper analyzes the power consumption and maximum demand to evaluate the effect of the power factor and forecast the value of the power consumption and maximum demand using support vector machine (SVM) regression in MATLAB software. The data on power consumption and maximum demand are collected from the previous 12-month electricity bills for the year 2019 until 2022 at Universiti Teknologi Mara (UiTM) campuses: Pulau Pinang, Arau, and Tapah. The graph of the maximum demand, power consumption, and power factor has been plotted to show the relationship between these three variables for the year 2022. From the graph, it was found that when the power factor is low, more current is needed to supply the same amount of power to a load. This means that more power is being drawn from the electrical supply system, which increases the power consumption. The power factor also shows the efficiency of the electricity at the UiTM. The quadratic SVM in regression learning is used to forecast the value of power consumption and maximum demand in MATLAB software without or with the use of the Principal Component Analysis (PCA). Therefore, it can show which one is better to use to make a prediction. This study successfully achieved the objective of analyzing power consumption, maximum demand, and power factor and forecasting the value of power consumption and maximum demand. © 2024 IEEE.