Open AccessElectrical peak load forecasting using long short term memory and support vector machine
Author(s) -
Muhammad Sadli,
Fajriana,
Wahyu Fuadi,
Ermatita Ermatita,
Iwan Pahendra
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/725/1/012060
Subject(s) - support vector machine , electrical load , term (time) , computer science , univariate , set (abstract data type) , series (stratigraphy) , time series , artificial intelligence , point (geometry) , machine learning , data mining , engineering , multivariate statistics , voltage , mathematics , electrical engineering , paleontology , physics , geometry , quantum mechanics , biology , programming language
Electrical load forecasting is usually a univariate time series forecasting problem. In this case, we use the machine learning approach based on Long Short Term Memory and Support Vector Machine. Accurate the peak electric load forecasting. The time series or data set of the peak electric load recorded from the Substation system in Lhoksumewe, Indonesia. The main aim of this paper to predict and evaluate the performance of peak electric load at the substation for six months. The results obtained in the study, the LSTM and SVM are proving useful for peak electrical load forecasting. The resulting point both of machine learning technique based on LSTM and SVM are a possibility for analysis data for such purposes.