Wind Power Prediction Based on Variational Autoencoder and Attention-Based Seq2Seq Model
A method for wind power prediction based on variational autoencoder and attention-based Seq2Seq model is proposed to address the complex characteristics of multiple influencing factors,limited amount of effective data,and long time series.The method involves collecting wind tower data and corresponding continuous power values to construct a sample set.Then,variational autoencoder model is used to augment the samples,and generate sufficient samples to support the training of the prediction model.A regression analysis model is built to explore the mapping relationship between multiple monitoring indicators from the wind towers and the continuous power values.The augmented different indicators are separately input into an attention-based Seq2Seq model for indicator time series prediction.Numerical weather forecast data is used to refine the prediction results,yielding more accurate weighted prediction results for the indicators.By inputting real-time wind tower data and numerical weather forecast data into the trained weighted prediction model and regression analysis model,multi-step wind power prediction can be achieved.The proposed method is validated using actual operational data from wind farms.The results show that compared to traditional time series predic-tion methods,the approach based on variational autoencoder and attention-based Seq2Seq model provides more accurate wind power predictions with smaller reconstruction errors.
variational autoencoderattention mechanismSeq2Seq-based modelwind power prediction
万方数据
维普
