Improving Electricity Price Forecasting Trough Data Segmentation based on Artificial Immune Systems

• Type: Article in International Conference Proceedings Book
• Date: 2018-06-29

Authors

José Nuno Fidalgo (Author) FEUP; Eduardo F. N. R. da Rocha (Author) Other

Conference proceedings International

Title: International Conference on the European Energy Market, EEM

15th International Conference on the European Energy Market, EEM 2018

27 June 2018 through 29 June 2018

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 1 Citation

Other information

Authenticus ID: P-00P-V26

DOI: 10.1109/eem.2018.8469954

Abstract (EN): The price evolution in electricity market with large share of renewables often exhibits a deep volatility, triggered by external factors such as wind and water availability, load level and also by business strategies of market agents. Consequently, in many real applications, the performance of electricity price is not appropriate. The goal of this article is to analyze the available market data and characterize circumstances that affect the evolution of prices, in order to allow the identification of states that promote price instability and to confirm that class segmentation allows increasing forecast performance. A regression technique (based on Artificial Neural Networks) was applied first to the whole set and then to each class individually. Performances results showed a clear advantage (above 20%) of the second approach when compared to the first one.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5