eProceedings of Engineering
Vol 8, No 6 (2021): Desember 2021

Prediction Of Electricity Use Using A Website-Based Support Vector Machine Algorithm

Reyhan Adiptya (Telkom University)
Muhhammad Ary Murti (Telkom University)
Casi Setianingsih (Telkom University)

Article Info

Publish Date
01 Dec 2021

Abstract

This study aims to create an electrical load prediction system using the Support Vector Machine algorithm to be able to predict future electrical loads. This study also finds out what parameters can reduce the error rate of predictions using Particle Swarm Optimization. Then everything is packaged into a website using the flask framework. The results of testing the parameters of the Support Vector Machine algorithm on the electricity usage prediction system, the lowest error values obtained are MAE, MSE, RMSE on the parameters of the PSO optimization results, the SVR parameter value is C = 1; Gamma=8.3; Epsilon=0.001; produces an error value, MAE=0.00829921; MSE=0.00602241; RMSE= 0.0776042. Keywords—Support Vector Machine, Particle Swarm Optimization, Prediction, Penggunaan Energi Listrik

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Journal Info
eProceedings of Engineering
Website

Abbrev

engineering

Publisher

Universitas Telkom

Subject

Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Description

Merupakan media publikasi karya ilmiah lulusan Universitas Telkom yang berisi tentang kajian teknik. Karya Tulis ilmiah yang diunggah akan melalui prosedur pemeriksaan (reviewer) dan approval pembimbing ...