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

Energies

Gerardo J. Osório¹  Juan M. Lujano-Rojas¹  João P. S. Catalão¹  Jorge N. D. L. Gonçalves² 

[1] C-MAST, University of Beira Interior, Covilhã 6201-001, Portugal;INESC TEC and the Faculty of Engineering of the University of Porto, Porto 4200-465, Portugal;

关键词: adaptive neuro-fuzzy inference system (ANFIS);    differential evolutionary particle swarm optimization (DEEPSO);    electricity market prices (EMP);    forecasting;    short-term;    time series;    wavelet transform (WT);    wind power;   

DOI  :  10.3390/en9090693

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown