Autors: Michanos S., Tsakoumis A., Fessas P., Vladov, S. S., Mladenov, V. M.
Title: Short-Term Load Forecasting Using a Chaotic Time Series
Keywords: chaos , load forecasting , multilayer perceptrons , time series

Abstract: A new approach to short-term load forecasting (STLF) in power systems is described in this paper. The method uses a chaotic time series and artificial neural network. The paper describes chaos time series analysis of daily power system peak loads. Nonlinear mapping of deterministic chaos is identified by multilayer perceptron (MLP). Using embedding dimension and delay time, an attractor in pseudo phase plane and an ANN model trained by this attractor are constructed. The proposed approach is demonstrated by an example.

References

    Issue

    Proceedings of the IEEE International Symposium on Signals, Circuits & Systems, pp. 437-440, 2003, Romania, IEEE

    Цитирания (Citation/s):
    1. 718) Castillo-Rojas, W. and Hernández, C., 2021. Bibliographic Review on Data Mining Techniques Used with Weather Data. Programming and Computer Software, 47(8), pp.817-829 (Web of Science). IF 0.936 - 2021 - в издания, индексирани в Scopus или Web of Science
    2. 719) Dialwar, U., Khaliq, A. and Kureshi, N., 2022. Evaluating Artificial Intelligence and Statistical Methods for Electric Load Forecasting. International Journal of Innovations in Science & Technology, 3(4), pp.59-83. (Google Scholar) - 2022 - от чужди автори в чужди издания, неиндексирани в Scopus или Web of Science

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