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

• Published in: Energy (Oxford) 2020-02, Vol.192, p.116498, Article 116498
• Main Authors: Li, Lei, Yin, Xiao-Li, Jia, Xin-Chun, Sobhani, Behrooz
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Computer simulation; Fuzzy clustering; Mathematical models; Methodology; Numerical weather forecasting; Optimization; Probabilistic forecast; Renewable energy; Simulation; Smart structures; Smell; SSO; Weather forecasting; Wind power; Wind speed
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2019.116498

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. •New forecasting approach based SSOFC-Apriori-WRP method is presented•1-valued WRP simulation is presented•Relationships of shape features and the forecasting error are considered•Converting process of wind speed forecast into power output is suggested