Enhanced Short‐Term Electricity Load Forecasting Using Meteorological Data and KCB‐Attention Network

Accurate power load forecasting is crucial for building a smart grid. The KCB‐Attention model is proposed in this paper to address the limitation of a single model for load forecasting and the complex coupling between seasonal meteorological data. In the feature engineering phase, sequence data reconstruction and alignment are performed for multidimensional weather‐load data, and seasonal pattern decomposition is performed by utilising a dynamic time‐sliding window strategy. In the forecasting model, by utilising convolutional neural networks (CNN) for efficient feature extraction, bidirectional long‐short‐term memory (BiLSTM) was introduced to improve the weight calculation of the self‐attention mechanism, and Keplerian optimisation algorithm (KOA) adaptively optimises network model hyper‐parameters for different seasonal datasets. This multi‐scale feature fusion strategy overcomes traditional models' limitations in handling nonlinear interactions between weather variables and load patterns. The results show that the KCB‐attention model outperforms existing methods in terms of forecasting accuracy. It has the potential to enhance the reliability of intelligent grid load forecasting.