Open AccessMachine Learning Approach for Electrical Load Forecasting Using Support Vector Regression
Author(s) -
Muhammad Sadli,
Fajriana,
Wahyu Fuadi,
Ermatita Ermatita,
Iwan Pahendra
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1361/1/012065
Subject(s) - computer science , support vector machine , grid , electricity , construct (python library) , electric power , electrical load , task (project management) , electricity market , electric power system , set (abstract data type) , power (physics) , artificial intelligence , engineering , electrical engineering , systems engineering , physics , geometry , mathematics , quantum mechanics , programming language
The management of power system in Lhokseumawe, Indonesia is complex task for transmission operator and is heavily reliant on knowledge of future energy demand. The available data allows for the maturation of the electricity market and encourages analysis of data to improve the generation, usage and management of electrical power. Our research specially will be based upon the Lhoksuemawe, Aceh data set which gives the total load on electric grid measured in intervals for past several years. In particular, our methods will use machine learning approaches by using support vector machine regression to forecast the average total load on Lhokseumawe, Aceh grid one day head of time. The results will be practically beneficial as utilities can use the predicted values to generate an adequate amount of energy to avoid grid outages and electrical losses as well as construct dynamic pricing schemes based upon future load.