A short-term power load prediction model based on improved wavelet neural networks

At present, the short-term power load prediction model generally uses the traditional wavelet neural network for prediction, which utilizes the gradient descent algorithm. However, it has the problems of sensitivity to the initial value and low prediction accuracy. To address this issue, we build a novel short-term power load prediction model leveraging the Wavelet Neural Network (WNN) base on the Comprehensive Improved Shuffled Frog Leaping Algorithm (CSFLA). By using this prediction model, we firstly conduct distributed storage and processing of a large amount of preprocessed historical load data, and then parallelize the processed historical load data by using MapReduce programming framework and WNN to obtain the prediction results. In the experiments, simulation results demonstrate that the proposed prediction model has high accuracy, strong adaptability and excellent parallel performance.