2005•Electric Power Science and EngineeringRequires access

Application of Optimal Combined Forecasting Method in Electricity Prices Forecasting

Gengyin Li

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Abstract

The electricity prices are the key factors of electricity markets.The electricity price forecasting is an important task which each market member concerns.To improve the accuracy of electricity price forecasting,the paper presents a combined forecasting method,which combines several single electricity price forecasting models together organically,and integrates the advantages of them to get more exact forecasting results.The optimal weight coefficients are determined by minimizing the sum of squared errors.California history daily average prices are applied to test the proposed approach.The result of the example analysis illustrates the validity of the combined forecasting method.

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What this paper is about

The electricity prices are the key factors of electricity markets.The electricity price forecasting is an important task which each market member concerns.To improve the accuracy of electricity price forecasting,the paper presents a combined forecasting method,which combines several single electricity price forecasting models together organically,and integrates the advantages of them to get more exact forecasting results.The optimal weight coefficients are determined by minimizing the sum of squared errors.California history daily average prices are applied to test the proposed approach.The result of the example analysis illustrates the validity of the combined forecasting method.

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Available abstract

The electricity prices are the key factors of electricity markets.The electricity price forecasting is an important task which each market member concerns.To improve the accuracy of electricity price forecasting,the paper presents a combined forecasting method,which combines several single electricity price forecasting models together organically,and integrates the advantages of them to get more exact forecasting results.The optimal weight coefficients are determined by minimizing the sum of squared errors.California history daily average prices are applied to test the proposed approach.The result of the example analysis illustrates the validity of the combined forecasting method.

Key concepts: Electricity price forecasting, Electricity, Electricity market, Probabilistic forecasting, Electricity price, Econometrics, Computer science, Key (lock)

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