Short-Term Electric Load Forecasting Using Wavelet Neural Networks

Wavelet Neural Networks (WNN) represent an intelligent algorithm that combines the strengths of wavelet analysis and neural networks, demonstrating unique value in short-term electric load forecasting. The core concept involves replacing traditional neural network activation functions with wavelet functions, leveraging the time-frequency localization characteristics of wavelet transforms to better capture non-stationary features in electric load sequences. In implementation, the process begins with preprocessing raw load data through operations including outlier handling, missing value imputation, and normalization. Subsequently, wavelet decomposition breaks down the load sequence into sub-sequences of different frequencies, typically containing trend components and high-frequency detail components. This multi-scale decomposition effectively separates deterministic and stochastic elements within load data. The network architecture typically employs a three-layer feedforward structure: input layer nodes correspond to the time window length of historical load data, the hidden layer uses wavelet functions like Morlet or Mexican Hat as activation functions, while the output layer features a single-node structure corresponding to the predicted load value at the target time. The training process utilizes error backpropagation algorithm, with special attention required for initialization strategies of wavelet basis function parameters. Compared to traditional BP neural networks, this method offers two main advantages: first, the multi-resolution characteristics of wavelet transforms effectively extract local features from load data; second, the compact support property of wavelet functions provides stronger generalization capability. Practical applications typically yield more stable prediction results compared to single-model approaches. In MATLAB implementation, key steps include constructing wavelet neural network objects, configuring training parameters, and designing cross-validation schemes. Notably, the selection of wavelet decomposition levels requires balancing computational complexity and prediction accuracy, with optimal decomposition scales typically determined through experimentation. Prediction results must undergo post-processing through inverse normalization to obtain final load values. Key implementation considerations involve: - Using wavedec function for multi-level wavelet decomposition - Implementing custom wavelet activation functions in the hidden layer - Applying trainlm or trainbr functions for network training with regularization - Designing time-series input buffers using buffer function for sliding window implementation - Employing mapminmax for data normalization and denormalization processes