Day-ahead electricity price forecasting using WT, MI and LSSVM optimized by modified ABC algorithm

H. Shayeghi, A. Ghasemi, M. Moradzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a novel hybrid algorithm to forecast day-ahead prices in the electricity market. Seeking for more accurate price forecasting techniques, this hybrid price-forecasting algorithm works based on Mutual Information (MI), Discrete Wavelet Transform (DWT), Least Squares Support Vector Machine (LSSVM) optimized by a Interactive Artificial Bee Colony (IABC) technique. The numerical simulation results show that the proposed hybrid algorithm improves the accuracy of electricity price forecasting in Spanish electricity market in comparison to previously-known classical and intelligent methods.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - 13th International Symposium on Neural Networks, ISNN 2016, Proceedings
PublisherSpringer Verlag
Pages454-464
Number of pages11
Volume9719
ISBN (Print)9783319406626
DOIs
Publication statusPublished - 2016
Event13th International Symposium on Neural Networks - St. Petersburg, Russian Federation
Duration: 6 Jul 20168 Jul 2016
Conference number: 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9719
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Symposium on Neural Networks
Abbreviated titleISNN 2016
CountryRussian Federation
CitySt. Petersburg
Period6/07/168/07/16

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  • Cite this

    Shayeghi, H., Ghasemi, A., & Moradzadeh, M. (2016). Day-ahead electricity price forecasting using WT, MI and LSSVM optimized by modified ABC algorithm. In Advances in Neural Networks - 13th International Symposium on Neural Networks, ISNN 2016, Proceedings (Vol. 9719, pp. 454-464). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9719). Springer Verlag. https://doi.org/10.1007/978-3-319-40663-3_52