Wind forecasting has gained considerable interest due to the abundance of renewable energy and the rapid advancement of wind energy extraction technologies. Wind forecasting is the process of extracting one or more features from time series data to increase prediction accuracy. The various forecasting models for wind speed and power include physical, statistical, computer, and hybrid models. The steps involved in forecasting wind speed and energy are preprocessing the raw data, feature extraction, and prediction. In this work, hybrid model prediction algorithms are combined to obtain better forecasting accuracy and maintain model efficacy and simplicity. The proposed model combines either autoregressive or autoregressive integrated moving average with cumulative Weibull distribution. The results demonstrated an improvement in short- and medium-term prediction when compared to other computational techniques such as Weibull, (AR), and autoregressive integrated moving average (ARIMA). Numerical error evaluation approaches such as Mean Absolute Percentage Error Mean Square Error, and Mean Absolute Error were used to forecast the model's correctness. The results indicated that the hybrid model's projected error is signification less than that of the AR and ARIMA models independently.