ICTACT Journal on Soft Computing

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An Elm for Bi-Classification of Vertically Bundled Electricity Market Prices


Affiliations
1 Department of Electrical Engineering, Annamalai University, India
2 Anubavam Technologies Private Limited, United States
     

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Electricity price forecasting is a challenging problem owing to the very great volatility of price which depends on many factors. This is especially prominent for both producers and consumers where a versatile price forecasting is crucial. This paper contributes an extreme learning machine (ELM) to classify the prices. These price classifications are essential since all market players do not know the precise value of future prices in their deciding procedure. In this paper, bi-classification model is proposed for prices utilizing the pre-specified price threshold. Three alternative classification models based on neural networks (NNs) are also proposed in bi-classification of prices. The performance of the proposed models is compared in terms of classification error and accuracy. The simulation results show that the ELM classification model is superior compared to three other classification models based on NNs. The performances of our models are evaluated using real data from vertically unbundled mainland Spain power system market.

Keywords

Electricity Price Classification, Extreme Learning Machines (ELM), Power System Market, Price Forecasting.
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  • An Elm for Bi-Classification of Vertically Bundled Electricity Market Prices

Abstract Views: 181  |  PDF Views: 3

Authors

S. Anbazhagan
Department of Electrical Engineering, Annamalai University, India
V. Sivakumar
Anubavam Technologies Private Limited, United States

Abstract


Electricity price forecasting is a challenging problem owing to the very great volatility of price which depends on many factors. This is especially prominent for both producers and consumers where a versatile price forecasting is crucial. This paper contributes an extreme learning machine (ELM) to classify the prices. These price classifications are essential since all market players do not know the precise value of future prices in their deciding procedure. In this paper, bi-classification model is proposed for prices utilizing the pre-specified price threshold. Three alternative classification models based on neural networks (NNs) are also proposed in bi-classification of prices. The performance of the proposed models is compared in terms of classification error and accuracy. The simulation results show that the ELM classification model is superior compared to three other classification models based on NNs. The performances of our models are evaluated using real data from vertically unbundled mainland Spain power system market.

Keywords


Electricity Price Classification, Extreme Learning Machines (ELM), Power System Market, Price Forecasting.

References