At present, operational power forecasts are primarily based on the predicted wind speed of a single valued deterministic Numerical Weather Prediction (NWP) simulation. However, due to the unavoidable uncertainties from model initialization andior model imperfections, recent numerical techniques cannot directly meet the actual needs of grid dispatch in many cases, which means that achieving accurate forecasts of wind speed and power is still a critical issue. On this topic, our paper contributes to the development of a new multi-step forecasting method termed CSFC-Apriori-WRF, providing a one-day ahead wind speed and power forecast consisting of 96 steps. This method is based on a Weather Research and Forecasting (WRF) simulation, a Cuckoo search (CS) optimized fuzzy clustering, and an Apriori association process. First, a wind speed forecast is generated by running a configured WRF model. Next, the wind speed forecasting series is divided into segments that meet certain conditions and are defined as "waves" in this paper. Next, combining the CS-optimized fuzzy clustering and Apriori algorithm, the proposed method extracts the association rules between the shape characteristics and the forecasting error of the divided waves. Applying the association rules in the final optimization process, the proposed method significantly reduces the uncertainties of the WRF simulation and performs best among other models to which it is compared. (C) 2017 Elsevier Ltd. All rights reserved.