Enhancing Electricity Price Prediction Accuracy With an Attention Mechanism‐ LSTM Hybrid Model

ABSTRACT The evolution of smart grid and the deepening of the power market have significantly enhanced the key position of precise electricity price estimation in industry decision‐making and the optimal resource allocation. It is necessary to implement short‐term electricity price forecast in the power market that accurately characterizes the dynamic change laws of electricity prices in a market environment with significant high volatility and nonlinear characteristics. To address this demand, the paper proposes an Attention‐based long short‐term memory model called ATT‐LSTM, which intends to improve the accuracy and interpretability of electricity price forecast, and provide support for power market operation analysis and trading decision making. In the model, multi‐dimensional feature engineering and data preprocessing are first performed, including meteorological data imputation, outlier removal, and time series sliding window construction. An attention layer is introduced to dynamically allocate importance weights to different time steps and features, allowing the model to adaptively focus on key influencing variables. This model is particularly suitable for electricity price forecast scenarios with significant seasonality and short‐term disturbances. Experiments are based on measured data from the Australian National Electricity Market, with comparisons conducted using multiple benchmark models, that is, LSTM, BPNN, and ATT‐LSTM. The results show that the ATT‐LSTM model achieves the optimal performance in terms of multiple metrics (e.g., MAE and RMSE) and prove that introducing the attention mechanism into the LSTM can effectively improve the accuracy and transparency of electricity price forecast.