Short-term load forecasting for distribution stations based on convolutional LSTM network considering attention mechanism

The significance of short-term load prediction for electrical distribution stations is paramount to ensuring the reliability and operational efficiency of power networks. This study introduces an integrated model that harnesses the strengths of convolutional neural networks (CNN), long short-term memory (LSTM) networks, and an attention mechanism to bolster the precision of short-term load forecasts. The CNN component is tasked with extracting spatial features from distribution station load data, thereby highlighting regional load patterns. Meanwhile, the LSTM network is responsible for deciphering the sequential dependencies within the historical load data, managing the evolution of load fluctuations over time. By integrating an attention mechanism, the model is empowered to concentrate on pivotal instances that significantly influence load alterations. Empirical evaluations utilizing an actual dataset from the power sector have validated the model's efficacy. The findings indicate that the proposed approach outperforms conventional prediction techniques in terms of accuracy, offering significant practical and theoretical advantages.