BP Neural Network for Short-Term Electric Load Forecasting

In the power industry, accurate electric load forecasting is crucial for ensuring safe and stable power supply. To achieve this objective, researchers have continuously explored various forecasting methodologies. Among these approaches, the Backpropagation (BP) neural network has emerged as one of the widely adopted techniques. This method employs normalized input data to perform short-term load predictions, typically forecasting daily loads based on weekly patterns. The implementation typically involves a multi-layer perceptron architecture where input neurons represent normalized historical load data, weather conditions, and temporal features. The hidden layers utilize sigmoid or ReLU activation functions to capture nonlinear relationships, while the output layer produces the predicted load values. Key algorithmic components include forward propagation for prediction and backward propagation for weight optimization using gradient descent. Although this method has certain limitations in handling extreme weather events or sudden demand changes, it remains an effective tool that enables power companies to better plan their electricity generation and distribution strategies. The neural network's ability to learn complex patterns from historical data helps utilities meet growing energy demands while maintaining grid stability. Common implementation considerations include data normalization techniques, network architecture optimization, and regularization methods to prevent overfitting.