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Akbar, Alfin Aulia
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Engineering

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Journal : Journal La Multiapp

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Prediction of Electrical Energy Needs for Capital City of Central Java Based on Backpropagation and Linear Regression Akbar, Alfin Aulia; Haddin, Muhammad
Journal La Multiapp Vol. 6 No. 2 (2025): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v6i2.2061

Abstract

This study discusses the prediction of electricity needs according to population growth. The model is determined by knowing the population and electricity needs. The parameters determined include: population, number of electricity consumers, energy consumption growth and electricity load factor for eleven years (2012-2023). The back propagation (BP) method and linear regression are used to help predict electricity needs for the next five years (2025-2030) with the BP architecture determined by three hidden layers and the number of neurons 12, 10 and 1. The object of the study was determined to be Semarang City, Indonesia. The results show that BP and linear regression can be used to predict electricity consumption needs in various sectors accurately. This is evidenced by the MAPE value below 10% and the MSE value of 2,65 x10-10 in the household sector, MSE 3,83 x 10-10 in the business sector, MSE 2,41 x 10-7 in the industrial sector, and MSE 3,6 x 10-12 in the public sector. The BP model produces predicted outputs of electrical energy in 2030 in the household sector of 1.104.140 MWH, the business sector of 843.757 MWH, the industrial sector of 1.027.790 MWH and the public sector of 375.974 MWH. The predicted increase in all sectors of electrical energy results in a total percentage of 54.21% for power sufficiency in 2030, so a thorough planning study is needed to meet electrical energy needs in that year.
Co-Authors Muhamad Haddin