Power System Load Forecasting Using Elman Neural Networks

To address power system load forecasting challenges, we developed an Elman neural network-based prediction methodology. The implementation includes comprehensive source data provision and robust program validation to ensure operational reliability, achieving excellent forecasting performance. Through constructing this Elman neural network-based load forecasting model, we enable accurate prediction of power system load patterns, providing critical reference support for system operation and dispatch scheduling. The algorithm implementation incorporates recurrent connections that maintain internal states, allowing the network to capture temporal dependencies in historical load data. Key functions include normalization preprocessing of time-series data, backpropagation through time (BPTT) training, and rolling-window prediction mechanisms. Application of this forecasting model facilitates improved power supply planning and management, enhancing system reliability and operational efficiency. Further model enhancements could involve hyperparameter optimization using genetic algorithms, inclusion of exogenous variables (weather data, economic indicators), and ensemble methods combining multiple neural architectures. In summary, the Elman neural network approach provides an effective and practical solution for power system load forecasting, with code structure supporting modular expansion for additional features like real-time adaptation and uncertainty quantification.