Electricity price forecasting on the day-ahead market using artificial intelligence algorithms

被引:0
作者
Galinska, Jolanta [1 ]
Terlikowski, Pawel [1 ]
机构
[1] Warsaw Univ Sci & Technol, Inst Elektroenergetyki, ul Koszykowa 75, PL-00662 Warsaw, Poland
来源
PRZEGLAD ELEKTROTECHNICZNY | 2024年 / 100卷 / 09期
关键词
artificial neural networks; electricity price forecasting; day-ahead market; TensorFlow;
D O I
10.15199/48.2024.09.29
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This article presents the application of AI algorithms to predict hourly product prices in the uniform price auction system of the Polish Power Exchange. It involves analyzing the electricity price determination process, identifying factors shaping price curves, reviewing literature on AI methods for electricity price prediction, proposing a research methodology, and modeling solutions using artificial neural networks. Nine variants of multilayer perceptrons with backpropagation were optimized and compared using most common indicators. Results were compared with forecasts from foreign articles for other European markets to evaluate the effectiveness of using AI in predicting electricity prices in the Polish Day-Ahead Market.
引用
收藏
页码:156 / 162
页数:7
相关论文
共 37 条
[1]  
[Anonymous], 2021, Energy Policy of Poland Until 2040
[2]  
[Anonymous], 2015, Act of June 12, 2015 on greenhouse gases emissions trading system
[3]  
[Anonymous], 2018, Act of 28 December 2018 amending the Act on Excise Duty and Certain Other Acts (Dz.U.2018poz.2538)
[4]  
[Anonymous], 2018, DIRECTIVE EU 2018 41
[5]  
Audytel, 2019, Analiza zmian hurtowych cen energii elektrycznej w 2018 r., dla: Krajowa Izba Gospodarcza Elektroniki i Telekomunikacji Analysis of Wholesale Electricity Price Changes in 2018
[6]  
Chaudhry Q., 2007, Quantitative StructureActivity Relationships (QSAR) for Pesticide Regulatory Purposes
[7]  
Chogumaira EN, 2009, T& D ASIA: 2009 TRANSMISSION & DISTRIBUTION CONFERENCE & EXPOSITION: ASIA AND PACIFIC, P106
[8]  
Dabrowska J., 2020, Impact of Unforeseen Events on Supply Chains on the Example of the COVID-19 Pandemic, V4
[9]  
Eseye AT, 2017, 2017 IEEE 2ND INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA), P552, DOI 10.1109/ICBDA.2017.8078695
[10]  
Geron A., 2019, Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems