Forecasting of Energy Consumption and Production Using Recurrent Neural Networks
Noman Shabbir, Lauri Kutt, Muhammad Jawad, Muhammad Naveed Iqbal, Payam Shams Ghahfaroki
DOI: 10.15598/aeee.v18i3.3597
Abstract
Energy forecasting for both consumption and production is a 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 are 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.