Forecasting next-day electricity prices by a neural network approach

D. Menniti, N. Scordino, N. Sorrentino
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引用次数: 5

Abstract

Forecasting short-term electricity market prices has been the focus of several studies in recent years. Although various approaches have been examined, achieving sufficiently low forecasting errors has not been always possible. However, certain applications, such as demand-side management, do not require exact values for future prices but utilize averages values as the basis for making short-term scheduling decisions. With the aim of enhancing the accuracy of the next-day electricity price forecasting, this paper proposes an approach to forecast the day-ahead electricity prices by means of n Artificial Neural Networks (ANNs), based on the estimation of the mean prices of n blocks of hours, with n identified according to the values of correlation factors computed on the basis of field records of the Italian electricity market. Simulation results show that forecasting next-day prices on an hourly basis induces to an error which results worse than the one made when average prices are forecasted according to groups of hours.
用神经网络方法预测次日电价
短期电力市场价格预测是近年来研究的热点问题。虽然研究了各种方法,但并不总是能够达到足够低的预测误差。然而,某些应用程序,如需求侧管理,不需要未来价格的精确值,而是利用平均值作为制定短期调度决策的基础。为了提高次日电价预测的准确性,本文提出了一种基于n个块小时平均电价估计的n个人工神经网络(ann)预测日前电价的方法,其中n根据意大利电力市场现场记录计算出的相关因子值确定。仿真结果表明,以小时为单位预测次日价格的误差比以小时为单位预测平均价格的误差更大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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