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

• Published in: Energies (Basel) Vol. 9; no. 9; p. 693
• Main Authors: Osorio, Gerardo J, N D L, Jorge, Lujano-Rojas, Juan M, Catalao, Joao P S
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 2016
• Subjects: adaptive neuro-fuzzy inference system (ANFIS); Adaptive systems; Alternative energy sources; Artificial neural networks; differential evolutionary particle swarm optimization (DEEPSO); Economic forecasting; Electricity; electricity market prices (EMP); Electricity pricing; Forecasting; Fuzzy logic; Genetic algorithms; Market prices; Markets; Neural networks; Optimization; Players; short-term; Support vector machines; Time series; wavelet transform (WT); Wavelet transforms; Wind power; Portugal; ANE, Portugal
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en9090693

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.