Day ahead powerful probabilistic wind power forecast using combined intelligent structure and fuzzy clustering algorithm

Lei Li, Xiao-Li Yin, Xin-Chun Jia and Behrooz Sobhani

Energy, 2020, vol. 192, issue C

Abstract: Nowadays, Operational power forecasts are associated with the one-value deterministic Numerical Weather Forecasting (NWF) simulation in the anticipated wind speed. This article introduced a novel predicting methodology called SSOFC-Apriori-WRP, which presents one-day-ahead wind power and speed forecasting. This methodology relies highly on a Weather Research and Prediction (WRP) simulation, a shark smell optimization (SSO), enhanced fuzzy clustering (EFC), and an Apriori association procedure. The wind speed prediction with the help of shaped WRP model was produced. Then by dividing wind speed predictions series into different parts, definite conditions were met and were introduced. Next the suggested methodology by the combination of SSO-optimized fuzzy clustering and Apriori algorithm withdraws the association rules, which are dominated among the anticipating errors in the divided waves and the shape features. The suggested methodology could be implemented to the other compared models and decrease the unreliability of the WRP simulation, if the association rules are used in the ultimate optimization process.

Keywords: Wind power; Probabilistic forecast; SSO; Fuzzy clustering (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:192:y:2020:i:c:s0360544219321930

DOI: 10.1016/j.energy.2019.116498

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