Short term electricity price forecast based on environmentally adapted generalized neuron

Nitin Singh, Soumya Ranjan Mohanty and Rishabh Dev Shukla

Energy, 2017, vol. 125, issue C, 127-139

Abstract: The liberalization of the power markets gained a remarkable momentum in the context of trading electricity as a commodity. With the upsurge in restructuring of the power markets, electricity price plays a dominant role in the current deregulated market scenario which is majorly influenced by the economics being governed. In the deregulated environment price forecasting is an important aspect for the power system planning. The problem of price forecasting can be entirely viewed as a signal processing problem with proper estimation of model parameters, modeling of uncertainties, etc. Among the different existing models the artificial neural network based models have gained wide popularity due their black box structure but it too has its own limitations. In the proposed work in order to overcome the limitations of the classical artificial neural network model, generalized neuron model is used for forecasting the short term electricity price of Australian electricity market. The pre-processing of the input parameters is accomplished using wavelet transform for better representation of the low and high frequency components. The free parameters of the generalized neuron model are tuned using environment adaptation method algorithm for increasing the generalization ability and efficacy of the model.

Keywords: Price forecasting; Generalized neuron model; Environment adaptation method; Wavelet transform; IEAM (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:125:y:2017:i:c:p:127-139

DOI: 10.1016/j.energy.2017.02.094

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