Forecasting of Energy Consumption and Production Using Recurrent Neural Networks

Advances in Electrical and Electronic Engineering

卷:18

Muhammad Naveed Iqbal¹  Noman Shabbir¹  Lauri Kutt¹  Payam Shams Ghahfaroki¹  Muhammad Jawad² 

[1] Department of Electrical Power Engineering & Mechatronics, School of Engineering, Tallinn University of Technology, Ehitajate tee 5, 12616 Tallinn, Estonia;

[2] Department of~Electrical Engineering, COMSATS University Islamabad (Lahore Campus), Defence Road, Off Raiwind Road, Lda Avenue Phase 1 Lda Avenue, Lahore, 54000 Punjab, Pakistan;

关键词: forecasting;    energy consumption;    energy generation;    machine learning;    neural networks.;   

DOI  :  10.15598/aeee.v18i3.3597

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown