A bat optimized neural network and wavelet transform approach for short-term price forecasting

A bat optimized neural network and wavelet transform approach for short-term price forecasting Applied Energy 210 (2018) 88–97 Contents lists available at ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy A bat optimized neural network and wavelet transform approach for short- term price forecasting P.M.R. Bento, J.A.N. Pombo, M.R.A. Calado, S.J.P.S. Mariano University of Beira interior and Instituto de Telecomunicações, Covilhã, Portugal HIGHLIGHTS GR APHICAL A BSTRACT We propose a new method for short- term price forecasting (STPF). The new method is based on Bat Algorithm, Wavelet Transform and Artificial Neural Networks. The method has the capability to auto- tune the best simulation parameters. We compare the proposed method in Spanish and Pennsylvania-New Jersey-Maryland (PJM) electricity markets. The proposed approach exhibits a better forecasting accuracy. ARTICLE I NFO ABSTRACT Keywords: In the competitive power industry environment, electricity price forecasting is a fundamental task when market Artificial neural networks participants decide upon bidding strategies. This has led researchers in the last years to intensely search for Bat algorithm accurate forecasting methods, contributing to better risk assessment, with significant financial repercussions. Scaled conjugate gradient This paper presents a hybrid method that combines similar and recent day-based selection, correlation and Short-term price forecasting wavelet analysis in a pre-processing stage. Afterwards a feedforward neural network is used alongside http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Energy Elsevier

A bat optimized neural network and wavelet transform approach for short-term price forecasting

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Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier Ltd
ISSN
0306-2619
D.O.I.
10.1016/j.apenergy.2017.10.058
Publisher site
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Abstract

Applied Energy 210 (2018) 88–97 Contents lists available at ScienceDirect Applied Energy journal homepage: www.elsevier.com/locate/apenergy A bat optimized neural network and wavelet transform approach for short- term price forecasting P.M.R. Bento, J.A.N. Pombo, M.R.A. Calado, S.J.P.S. Mariano University of Beira interior and Instituto de Telecomunicações, Covilhã, Portugal HIGHLIGHTS GR APHICAL A BSTRACT We propose a new method for short- term price forecasting (STPF). The new method is based on Bat Algorithm, Wavelet Transform and Artificial Neural Networks. The method has the capability to auto- tune the best simulation parameters. We compare the proposed method in Spanish and Pennsylvania-New Jersey-Maryland (PJM) electricity markets. The proposed approach exhibits a better forecasting accuracy. ARTICLE I NFO ABSTRACT Keywords: In the competitive power industry environment, electricity price forecasting is a fundamental task when market Artificial neural networks participants decide upon bidding strategies. This has led researchers in the last years to intensely search for Bat algorithm accurate forecasting methods, contributing to better risk assessment, with significant financial repercussions. Scaled conjugate gradient This paper presents a hybrid method that combines similar and recent day-based selection, correlation and Short-term price forecasting wavelet analysis in a pre-processing stage. Afterwards a feedforward neural network is used alongside

Journal

Applied EnergyElsevier

Published: Jan 15, 2018

References

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