Order reprints

Short-term load forecasting using machine learning and periodicity decomposition

Abdelkarim El khantach Mohamed Hamlich Nour eddine Belbounaguia

*Corresponding author: Abdelkarim El khantach k.abdelkarim@gmail.com


The accuracy of electricity consumption forecasts is of paramount importance in energy planning, it provides strong support for the effective energy demand management. In this work, we proposed a load forecast through the decomposition of the historical time series in relation to the historical evolution of each hour of the day. The output of these decomposition were served as input to different algorithms of machine learning. We tested our model by five machines learning methods, the achieved results are examined with three of the most commonly used evaluation measures in forecasting. The obtained results were very satisfactory.

Please supply your name and a valid email address you yourself

Fields marked*are required

Article URL   http://www.aimspress.com/energy/article/3894.html
Article ID   energy-07-03-382
Editorial Email  
Your Name *
Your Email *
Quantity *

Copyright © AIMS Press All Rights Reserved