Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model

Karol Pilot, Alicja Ganczarek-Gamrot () and Krzysztof Kania
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Karol Pilot: Independent Researcher, 40-287 Katowice, Poland
Alicja Ganczarek-Gamrot: Faculty of Informatics and Communication, University of Economics in Katowice, 40-287 Katowice, Poland
Krzysztof Kania: Faculty of Informatics and Communication, University of Economics in Katowice, 40-287 Katowice, Poland

Energies, 2024, vol. 17, issue 17, 1-20

Abstract: Forecasting the electricity market, even in the short term, is a difficult task, due to the nature of this commodity, the lack of storage capacity, and the multiplicity and volatility of factors that influence its price. The sensitivity of the market results in the appearance of anomalies in the market, during which forecasting models often break down. The aim of this paper is to present the possibility of using hybrid machine learning models to forecast the price of electricity, especially when such events occur. It includes the automatic detection of anomalies using three different switch types and two independent forecasting models, one for use during periods of stable markets and the other during periods of anomalies. The results of empirical tests conducted on data from the Polish energy market showed that the proposed solution improves the overall quality of prediction compared to using each model separately and significantly improves the quality of prediction during anomaly periods.

Keywords: prediction; energy market; anomalies; hybrid models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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