Adaptive Interval Forecasting of Wind Power Based on Diffusion Model and Ramping Trend Classification
Diffusion models,based on the probabilistic properties of Markov chains,can quantitatively describe the stochasticity and uncertainty of wind power.However,the traditional diffusion model-based time-series forecasting method uses the average of the current input previous period samples as the benchmark for feature scaling,resulting in a forecasting interval that is too large during peak periods and too small during trough periods.Thus,an adaptive interval forecasting method for wind power based on diffusion model and ramping trend classification is proposed.First,the diffusion model-based interval forecasting framework obtains the initial forecasting intervals.Then,the wind power fluctuation process is divided into six patterns,and an adaptive regularization strategy is adopted for the forecasting intervals under different patterns,to obtain the initial improved intervals.Then,to address the problem of interval bandwidth mismatch during non-ramping periods in high output patterns,an evaluation model for ramping trend classification is established,and the intervals are corrected by combining them with the affiliated output patterns to obtain the final interval forecasting results.Finally,the experimental results show that the interval forecasting effect of the proposed method is better.
万方数据
维普
