Energy consumption prediction for electric rickshaw based on load variance

Abstract

Electrical Rikshaw forecasting is usually a univariate time series forecasting problem. The recent increase in load variance of electric rickshaw has lead to large available datasets. In this case, we use the machine learning approach based on Long Short Term Memory (LSTM) and Support Vector Machine (SVM). The accurate energy consumption of electric Rickshaw forecasting is depended to train and test real dataset. The time series of electric Rickshaw observed in Mechanical Labs of Universitas HKBP Nomensen, Medan, Indonesia. The main aim of this paper to generate the observation of energy consumption from the electric rickshaw and look for accuracy of electrical load in the substation.