Forecasting Day-Ahead Electricity Price with Artificial Neural Networks: a Comparison of Architectures

被引：2

作者：

Pavicevic, Milutin ^{[1
]}

Popovic, Tomo ^{[1
]}

机构：

[1] Univ Donja Gorica, Fac Informat Syst & Technol, Oktoih 1, Podgorica 81000, Montenegro

来源：

PROCEEDINGS OF THE 11TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS: TECHNOLOGY AND APPLICATIONS (IDAACS'2021), VOL 2 | 2021年

基金：

欧盟地平线“2020”;

关键词：

Artificial neural networks; day-ahead electricity price; forecasting; convolutional neural networks; recurrent neural networks;

D O I：

10.1109/IDAACS53288.2021.9660955

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The spot price prediction for the electric energy markets is a widely approached problem, used by many participants in the market. The ever-shifting rules and regulations, rising percentage of the electricity on the market being produced by solar and wind plants and many stochastic factors influencing it make the market price of electricity very volatile and hard to forecast. Many methods are used to tackle this problem, and their efficiency varies from dataset to dataset. In this work, we use the dataset of hourly day-ahead spot prices from the Hungarian HUPX market, and couple it with weather data for Hungary. We test various types of Dense, Recurrent and Convolutional neural network architectures and report on the results.

引用

页码：1083 / 1088

页数：6

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