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
Applied Energy – Elsevier
Published: Jan 15, 2018
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