Modeling and Forecasting Electricity Price Based on Multi Resolution Analysis and Dynamic Neural Networks

Modeling and Forecasting Electricity Price Based on Multi Resolution Analysis and Dynamic Neural Networks

Salim Lahmiri (ESCA School of Management, Morocco & University of Quebec at Montreal, Canada)
Copyright: © 2015 |Pages: 13
DOI: 10.4018/978-1-4666-5888-2.ch628
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Background

The purpose of this study is to forecast California electricity price using artificial neural networks (ANN) and continuous wavelet transform (CWT) as multi resolution technique to analyze the original data to extract hidden patterns. In particular, the conventional backpropagation neural network (BPNN), the Elman recurrent neural network, the time delay recurrent neural network (TDNN), and the adaptive time delay recurrent neural network (ATDNN) were used as predictive systems. The TDNN and ATDNN integrate genetic algorithms (GA) to optimize their respective architecture.

Key Terms in this Chapter

Artificial Neural Networks (ANN): intelligent systems that mimic the processing of information by human brain neurons. They are capable of learning attributes, generalizing, parallel processing of information, and error minimization.

Autoregressive Moving Average (ARMA) Process: a statistical model used to find a parsimonious and a linear behavior of a given data.

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