Forecasting of Energy Consumption and Production Using Recurrent Neural Networks

Advances in Electrical and Electronic Engineering 2020
Noman shabbir, Lauri Kütt, Muhammad JAWAD, Muhammad Naveed Iqbal, Payam Shams Ghahfarokhi

Energy forecasting for both consumption and production is a very challenging task as it involves many variable factors. It is necessary to calculate the actual production of energy and its consumption as it is very beneficial in maintaining demand and supply. The reliability and smooth functioning of any electrical system is dependent on this management. In this article, the Recurrent Neural Network (RNN) based algorithm is used for energy forecasting. The algorithm is used for making three days ahead prediction of energy for both generation and consumption in Estonia. A comparison is also made between our proposed algorithm and the forecasting algorithm used by Estonian energy regulatory authority. The results of both algorithms indicate that our proposed algorithm has lower Root Mean Square Error (RMSE) and is giving better forecasting.

Atslēgas vārdi
Forecasting, Energy Consumption, Energy Generation, Machine Learning, Neural Networks.
DOI
10.15598/aeee.v18i3.3597

Shabbir, N., Kütt, L., Jawad, M., Iqbal, M., Shams Ghahfarokhi, P. Forecasting of Energy Consumption and Production Using Recurrent Neural Networks. Advances in Electrical and Electronic Engineering, 2020, Vol. 18, No. 3, 190.-197. lpp. ISSN 1336-1376. e-ISSN 1804-3119. Pieejams: doi:10.15598/aeee.v18i3.3597

Publikācijas valoda
English (en)