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A neural network approach to day-ahead deregulated electricity market prices classification

机译：神经网络方法对日前放松管制的电力市场价格分类

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摘要

This paper proposes a day-ahead electricity price classification that could be realized using three-layered feed forward neural network (FFNN), cascade-forward neural network (CFNN) trained by the Levenberg-Marquardt (LM) algorithm and generalized regression neural network (GRNN). The electricity price classification method is as an alternative to numerical electricity price forecasting due to high forecasting errors in various approaches. These electricity price classifications are important because all market participants do not know the exact value of future prices in their decision-making process. In this paper, various electricity market price classification classes with respect to pre specified electricity price thresholds are used. The simulation results show that the proposed CFNN method provides a robust and accurate method for day-ahead deregulated electricity market price classification classes. The proposed neural network classification models of electricity prices are tested on the electricity markets of mainland Spain and New York.
机译：本文提出了可以使用三层前馈神经网络（FFNN），由Levenberg-Marquardt（LM）算法训练的级联前向神经网络（CFNN）和广义回归神经网络来实现的提前电价分类（ GRNN）。由于各种方法中的高预测误差，因此电价分类方法可替代数字电价预测。这些电价分类很重要，因为所有市场参与者都不知道其决策过程中未来电价的确切价值。在本文中，使用了针对预定电价阈值的各种电价分类。仿真结果表明，所提出的CFNN方法为日前放松管制的电力市场价格分类提供了一种鲁棒而准确的方法。拟议的电价神经网络分类模型在西班牙大陆和纽约的电力市场上进行了测试。

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