基于贝叶斯优化注意力机制 LSTNet 模型的 短期电力负荷预测.

In order to more fully exploit the effective information among multivariate load series and thus improve the prediction accuracy, a short-term load prediction model combining Bayesian optimization algorithm, long and short-term time series network with attention mechanism and error correction was proposed. First, the LSTNet-attention model based on Bayesian optimization was constructed for preliminary prediction, and the Bayesian algorithm was used to optimize multiple structural parameters of the model, reduce the randomness of manually set parameters, and reasonably assign feature weights through the attention mechanism. Then, an XGBoost error correction model based on Bayesian hyperparameter optimization was established to mine the potential, unused and effective information in the initial prediction error sequence, and make error prediction and correction, and then obtain the final prediction results. Through the empirical analysis using the real load data of a place in Australia, the experimental results show that the proposed prediction model has better prediction effect compared with other models, which provides a relevant reference for research work such as load prediction. [ABSTRACT FROM AUTHOR]