Application of Elman Neural Network in Electric Power Load Forecasting Model Research

As a typical local recurrent global feedback neural network, the Elman neural network demonstrates unique advantages in the field of electric power load forecasting. Power load prediction serves as a critical component in smart grid system operation and scheduling, where accurate forecasting of future electricity demand is essential for ensuring grid stability and economic efficiency.

Compared to traditional feedforward neural networks, the Elman neural network incorporates a context layer to store historical state information, enabling superior processing of time-dependent power load data. This architecture proves particularly effective in capturing nonlinear patterns of electricity load variations over time, including daily周期性 and seasonal fluctuation characteristics. From an implementation perspective, the context layer can be coded as a delay unit that maintains hidden layer activations from previous time steps, often implemented using recurrent connections with fixed weights of 1.0.

During model construction, the Elman neural network typically utilizes multidimensional features such as historical load data, weather factors, and economic indicators as input variables. Network training employs the error backpropagation algorithm (specifically Backpropagation Through Time for recurrent networks), which minimizes prediction errors by adjusting connection weights through gradient descent optimization. The training process involves forward propagation of input sequences, calculation of output errors, and backward propagation of these errors to update weight matrices between input-hidden, context-hidden, and hidden-output layers. This dynamic memory capability allows the model to adaptively learn temporal patterns of load variations without requiring predefined mathematical relationships like conventional statistical methods.

Practical applications demonstrate that the Elman neural network achieves high accuracy in short-term load forecasting (such as hourly or daily predictions). Key implementation considerations include proper normalization of input data, optimal selection of hidden layer neurons (typically determined through cross-validation), and appropriate time lag settings for the context layer. The prediction results provide decision support for power dispatch departments, facilitating optimized generation scheduling and reduced power generation costs. Furthermore, with the widespread adoption of smart meters and accumulation of extensive historical data, the forecasting precision of this method continues to improve through enhanced training datasets and refined network architectures.