Short-term power load forecasting based on BiGRU-Attention-SENet model

Abstract

ABSTRACT Due to the randomness of weather conditions, the complexity of power demands and the nonlinearity of high-dimensional data, traditional regression analysis, and time series forecasting methods can no longer meet the current requirements of high-precision wind power load forecasting. Therefore, it is necessary to use an accurate short-term load forecasting method for power grid dispatching and planning. Taking the onshore wind power load data of Valencia, Spain (from January 1, 2015, to December 31, 2018), as the research object, this paper proposes a hybrid model of bidirectional gated recurrent units and squeeze-and-excitation networks (BiGRU-SENet) based on an attention mechanism, which is good at handling the nonlinearity of high-dimensional time series data, to predict short-term power loads. The results showed that (1) the attention mechanism could significantly improve the predictive performance of BiGRU by weighting the input of different time steps. (2) The GRU-CNN was more suitable for short-term load forecasting than CNN-GRU, which could make full use of time series information and not lose the key information at the beginning. (3) SENet enabled CNN to perform feature-wise recalibration, further focusing on effective features. (4) Compared with the second-best model (BiGRU-Attention-CNN), BiGRU-Attention-SENet was 12.849% and 3.096% lower in terms of the mean absolute percentage error (MAPE) and root mean square error (RMSE), respectively. In terms of computational overheads, BiGRU-Attention-SENet achieved greater improvement in prediction accuracy by adding relatively fewer computational overheads (the parameters increased by 0.338%, and the FLOPs increased by 0.621%). (5) Through fine tuning, the pre-trained model achieved almost the same accuracy as the no pre-trained one in a shorter time, which proved that BiGRU-Attention-SENet had strong generalization ability. The hybrid model proposed in this paper has application prospects in power load forecasting.