Autors: Galarniotis A.I., Tsakoumis A.C., Fessas P., Vladov, S. S., Mladenov, V. M.
Title: Using Elman and FIR neural networks for short term electric load forecasting
Keywords: Elman network; Finite impulse response (FIR) network; Multil

Abstract: Finite impulse response (FIR) neural network and Elman neural network have been compared in electric load prediction. An FIR neural network has been trained with a temporal back-propagation learning algorithm and the results obtained showed that the effectiveness of the algorithm is more important than the applied network model. The comparison between both networks and the standard approach with Multilayer perceptron (MLP) network, demonstrates that the FIR network acts adequately. It performs better than the Elman network. Both networks perform better than the MLP network.

References

    Issue

    SCS 2003 - International Symposium on Signals, Circuits and Systems, Proceedings, vol. 2, pp. 433-436, 2003, Romania, IEEE, DOI 10.1109/SCS.2003.1227082

    Цитирания (Citation/s):
    1. Pratapa Raju, M., and A. Jaya Laxmi. "Implementation of Load Demand Prediction Model for a Domestic Load Center Using Different Machine Learning Algorithms—A Comparison." In Pervasive Computing and Social Networking, pp. 445-467. Springer, Singapore, 2022. - 2022 - в издания, индексирани в Scopus или Web of Science
    2. Sharifzadeh M., Sikinioti-Lock A., Shah N., Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression, Renewable and Sustainable Energy Reviews 108, pp. 513-538, doi.org/10.1016/j.rser.2019.03.040?, 2019. (Scopus) - 2019 - в издания, индексирани в Scopus или Web of Science

    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus