EGA-STLF: A Hybrid Short-Term Load Forecasting Model

Abstract

As the development of smart grids and electricity markets around the world, short-term load forecasting (STLF) plays an increasingly important role in safe and economical operations of power systems. Facing massive multivariate and heterogeneous data from smart grid environment, traditional STLF methods no longer meet the requirements of comprehensive prediction performance in big data era. Therefore, a novel STLF model named EGA-STLF is proposed in this paper. The core idea of the proposed model is mainly based on distributed representation, bidirectional gated recurrent unit (Bi-GRU), and attention mechanism. EGA-STLF encodes non-numerical variables in input data into sparse vectors by distributed representation during preprocessing, which can more reasonably reflect the correlations between different variables across the temporal sequence. Moreover, the Bi-GRU layer in EGA-STLF processes the past and the future information simultaneously to fully extract temporal and nonlinear features from input data for the improvement of forecasting accuracy. In addition, the introduction of attention mechanism highlights the role of key features in load forecasting, which is beneficial to generate more accurate forecasting results for EGA-STLF. The validity and superiority of EGA-STLF is verified by taking the actual data from Victoria state and Queensland state in Australia for experimental research in which mean absolute percentage error (MAPE) and root mean square error (RMSE) are selected as evaluation metrics. The experimental results indicate that EGA-STLF outperforms the state-of-the-art models based on SVR and MLP in term of comprehensive forecasting accuracy. Hence, EGA-STLF is a promising method to create economic benefits in power industry.

Keywords

• Attention mechanism
• bidirectional gated recurrent unit
• distributed representation
• deep learning
• short-term load forecasting