Wind speed prediction for small sample dataset using hybrid first-order accumulated generating operation-based double exponential smoothing model

Abstract

Wind power generation has recently emerged in many countries. Therefore, the availability of long-term historical wind speed data at various potential wind farm sites is limited. In these situations, such forecasting models are needed that comprehensively address the uncertainty of raw data based on small sample size. In this study, a hybrid first-order accumulated generating operation-based double exponential smoothing (AGO-HDES) model is proposed for very short-term wind speed forecasts. Firstly, the problems of traditional Holt's double exponential smoothing model are highlighted considering the wind speed data of Palmerston North, New Zealand. Next, three improvements are suggested for the traditional model with a rolling window of six data points. A mixed initialization method is introduced to improve the model performance. Finally, the superiority of the novel model is discussed by comparing the accuracy of the AGO-HDES model with other forecasting models. Results show that the AGO-HDES model increased the performance of the traditional model by 10%. Also, the modified model performed 7% better than other considered models with three times faster computational time.