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Innovations-based Neural Network Seasonal Day-ahead Marginal Price Forecasting

机译：基于创新的神经网络季节性超前边际价格预测

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摘要

Successful bidding and operational strategies of electric power generators (GENCO) depend highly on the availability of accurate and timely load and price forecasts. Several techniques have been proposed and applied over the past few years to predict the marginal price of electricity in deregulated markets. To improve accuracy, these techniques apply time-consuming, complex, and hybrid methods requiring multiple inputs and large databases. This article introduces the first application of the method of "innovations" and a single artificial neural network to provide accurate forecasting results with mean absolute percentage error comparable to more complex and hybrid artificial neural network forecasting methods. The proposed model is applied to data of two seasons of Spain's power market operator (OMEL) marginal price data. The technique provided average accuracy improvement of 26% with overall mean absolute percentage error of 6.5%, which is reasonable considering the number of inputs and the simplicity of this model compared to other proposed models.
机译：发电机（GENCO）的成功投标和运营策略在很大程度上取决于准确，及时的负荷和价格预测的可用性。在过去的几年中，已经提出并应用了几种技术来预测放松管制的市场中的边际电价。为了提高准确性，这些技术应用了耗时，复杂和混合的方法，这些方法需要多个输入和大型数据库。本文介绍“创新”方法的首次应用和单个人工神经网络，以提供可与更复杂和混合人工神经网络预测方法相比的平均绝对百分比误差的准确预测结果。提议的模型应用于西班牙电力市场运营商（OMEL）两个季节的边际价格数据。该技术提供了26％的平均准确度改进，总体平均绝对百分比误差为6.5％，考虑到输入数量和与其他拟议模型相比该模型的简单性，这是合理的。

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