Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Osorio, GJ (Author) Other; Goncalves, JNDL (Author) Other; Lujano Rojas, JM (Author) Other; Catalao, JPS (Author) FEUP

Journal

Title: Energies

Vol. 9

Final page: 693

ISSN: 1996-1073

Publisher: MDPI

Indexing

ISI Web of Knowledge - 15 Citations

Scopus - 18 Citations

Other information

Authenticus ID: P-00K-WB4

DOI: 10.3390/en9090693

Abstract (EN): The uncertainty and variability in electricity market price (EMP) signals and players' behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately support their decisions in a competitive environment with lower forecasting error and with an acceptable computational time. As previously published methodologies have shown, hybrid approaches are good candidates to overcome most of the previous concerns about time-series forecasting. In this sense, this paper proposes an enhanced hybrid approach composed of an innovative combination of wavelet transform (WT), differential evolutionary particle swarm optimization (DEEPSO), and an adaptive neuro-fuzzy inference system (ANFIS) to forecast EMP signals in different electricity markets and wind power in Portugal, in the short-term, considering only historical data. Test results are provided by comparing with other reported studies, demonstrating the proficiency of the proposed hybrid approach in a real environment.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 19