A deterministic and probabilistic framework based on corrected wind speed to improve Short-Term wind power forecasting accuracy - Nuttapat Jittratorn; Chao-Ming Huang; Hong-Tzer Yang (International Journal of Electrical Power & Energy Systems, 2025)
The safety and stability of power systems with high wind power penetration are challenged by the inherent variability and uncertainty of wind generation. To improve short-term wind power forecasting accuracy, this study proposes a novel deterministic and probabilistic forecasting framework based on corrected wind speed, integrating a wind speed correction method, a bidirectional long short-term memory network, Markov chain-based integration, and jackknife resampling. The novelty lies in combining these components to enhance input data quality and predictive performance. Each component contributes as follows: the wind speed correction reduces input error, improving input accuracy; the bidirectional long short-term memory enhances time-series learning to better capture temporal dependencies; the Markov chain integration improves deterministic forecasts by modeling transition probabilities; and the jackknife resampling quantifies prediction uncertainty to improve probabilistic forecast reliability. Using real-world data from a 3.6 MW wind power plant in Changhua, Taiwan, the proposed model reduces the mean absolute error of forecasted wind speed from 3.93 m/s to 0.78 m/s. The model achieves a mean relative error of 5.89 %, representing a 49.27 % improvement over the model without wind speed correction and outperforming other benchmark deterministic forecasting models. In probabilistic forecasting, the model attains a prediction interval coverage probability of 95.14 %, improving by 11.90 % over the model without wind speed correction and surpassing other benchmark probabilistic models. These results confirm the effectiveness of the proposed approach in enhancing both deterministic and probabilistic forecasting accuracy, thereby supporting more reliable power system operations.