Optimized Calculation of Hourly Price Forward Curve (HPFC)
This paper examines many mathematical methods for
molding the hourly price forward curve (HPFC); the model will be
constructed by numerous regression methods, like polynomial
regression, radial basic function neural networks & a furrier series.
Examination the models goodness of fit will be done by means of
statistical & graphical tools. The criteria for choosing the model will
depend on minimize the Root Mean Squared Error (RMSE), using the
correlation analysis approach for the regression analysis the optimal
model will be distinct, which are robust against model
misspecification. Learning & supervision technique employed to
determine the form of the optimal parameters corresponding to each
measure of overall loss. By using all the numerical methods that
mentioned previously; the explicit expressions for the optimal model
derived and the optimal designs will be implemented.
[1] iEnergy Australia Pty Ltd, Brisbane, Australia , (www.ienergy.com.au )
[2] Pilipovic Dragana.1997. Valuing and Managing Energy Derivatives.
McGraw-Hill.
[3] Alexander Eydeland &Krzysztof Wolynie.2003.Energy and Power Risk
Management. John Wiley &Sons, Inc.
[4] Kecman Vojislav .2001.Learning and Soft Computing. A Bradford
Book, the MIT Press. Cambridge, Massachusetts.
[5] Sadeghi and Ware," Mean Reverting Models for Energy Option
Pricing," Working Paper, University of Calgary, 2001.
[6] Panagiotis A. Dafas, "Estimating the parameters of a mean-reverting
Markov- switching jump-diffusion model for crude oil spot prices"
Working Paper, University of Calgary, 2004.
[7] Alvaro Cartea and Marcelo G. Figueroa," Pricing in Electricity Markets:
a mean reverting jump diffusion model with seasonality", Working
Paper, University of London, 2005.
[8] Lei Xiong," Stochastic Models for Electricity Prices", Working Paper,
University of Calgary, 2004.
[9] Patrick MacDonald Patrick," Contingent Claims in the Alberta
Electricity Market". Working Paper, University of Calgary.2003.
[10] The Information Technology Laboratory (ITL) at the National Institute
of Standards and Technology (NIST). (http://www.itl.nist.gov)
[11] http://www.sixsigmafirst.com/Simple_regression_analysis.htm
[12] http://en.wikipedia.org/wiki/Nonlinear_regression
[1] iEnergy Australia Pty Ltd, Brisbane, Australia , (www.ienergy.com.au )
[2] Pilipovic Dragana.1997. Valuing and Managing Energy Derivatives.
McGraw-Hill.
[3] Alexander Eydeland &Krzysztof Wolynie.2003.Energy and Power Risk
Management. John Wiley &Sons, Inc.
[4] Kecman Vojislav .2001.Learning and Soft Computing. A Bradford
Book, the MIT Press. Cambridge, Massachusetts.
[5] Sadeghi and Ware," Mean Reverting Models for Energy Option
Pricing," Working Paper, University of Calgary, 2001.
[6] Panagiotis A. Dafas, "Estimating the parameters of a mean-reverting
Markov- switching jump-diffusion model for crude oil spot prices"
Working Paper, University of Calgary, 2004.
[7] Alvaro Cartea and Marcelo G. Figueroa," Pricing in Electricity Markets:
a mean reverting jump diffusion model with seasonality", Working
Paper, University of London, 2005.
[8] Lei Xiong," Stochastic Models for Electricity Prices", Working Paper,
University of Calgary, 2004.
[9] Patrick MacDonald Patrick," Contingent Claims in the Alberta
Electricity Market". Working Paper, University of Calgary.2003.
[10] The Information Technology Laboratory (ITL) at the National Institute
of Standards and Technology (NIST). (http://www.itl.nist.gov)
[11] http://www.sixsigmafirst.com/Simple_regression_analysis.htm
[12] http://en.wikipedia.org/wiki/Nonlinear_regression
@article{"International Journal of Electrical, Electronic and Communication Sciences:52124", author = "Ahmed Abdolkhalig", title = "Optimized Calculation of Hourly Price Forward Curve (HPFC)", abstract = "This paper examines many mathematical methods for
molding the hourly price forward curve (HPFC); the model will be
constructed by numerous regression methods, like polynomial
regression, radial basic function neural networks & a furrier series.
Examination the models goodness of fit will be done by means of
statistical & graphical tools. The criteria for choosing the model will
depend on minimize the Root Mean Squared Error (RMSE), using the
correlation analysis approach for the regression analysis the optimal
model will be distinct, which are robust against model
misspecification. Learning & supervision technique employed to
determine the form of the optimal parameters corresponding to each
measure of overall loss. By using all the numerical methods that
mentioned previously; the explicit expressions for the optimal model
derived and the optimal designs will be implemented.", keywords = "Forward curve, furrier series, regression, radial basic
function neural networks.", volume = "2", number = "9", pages = "1834-11", }