Comparison of day-ahead price forecasting in energy market using Neural Network and Genetic Algorithm

Author

Sarada, K. ; Bapiraju, V.

Author_Institution

Dept. of Electr. & Electron. Eng., K.L. Univ., Guntur, India

fYear

2014

fDate

19-20 Sept. 2014

Firstpage

1

Lastpage

5

Abstract

Price prognostication has become progressively relevant to producers and shoppers within the new competitive wattage markets. Both for spot markets and long term contractors, value forecasts square measure necessary to develop bidding ways. In this paper, Genetic Algorithm based Neural network (GANN) approach is used to forecast short term hourly electricity price and the results are compared with Aritificial Neural Network(ANN). The recommended method is studied on the PJM electricity market. The results achieved through the simulation illustrates that the proposed model offers exact and improved results.

Keywords

genetic algorithms; load forecasting; neural nets; power engineering computing; power markets; tendering; ANN; GANN; PJM electricity market; aritificial neural network; bidding way; competitive wattage market; day-ahead price forecasting; electricity price prognostication; energy market; genetic algorithm based neural network; price prognostication; spot market; value forecast square measure; Artificial neural networks; Biological cells; Electricity; Forecasting; Genetic algorithms; Load modeling; Predictive models; ANN; Electricity market; Genetic Algorithm; price forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Smart Electric Grid (ISEG), 2014 International Conference on

Conference_Location

Guntur

Print_ISBN

978-1-4799-4104-9

Type

conf

DOI

10.1109/ISEG.2014.7005607

Filename

7005607