Short-term household load forecasting based on Stacking-SCN

Abstract

The household load has high volatility and uncertainty, which plays an increasingly important role in short-term power load forecasting for individual residential customers in the operation and planning of future power grids. There is still room for improvement in the prediction accuracy of traditional machine learning and deep learning models. This paper firstly analyzes the complexity of household load data and formulates forecasting problems. Then, the principle of stochastic configuration network (SCN), stacking algorithm and a stacking integrated stochastic configuration network (Stacking-SCN) is introduced, which constructs a short-term household load forecasting model. By adaptively constraining the output weight distribution of the network model, the prediction accuracy can be improved. To show the prediction performance of the algorithm, the experiment comparing the Stacking-SCN model with incremental random vector functional link network (IRVFLN) models, the traditional SCN, ensemble-SCN (E-SCN), deep neural networks (DNN) and long short-term memory (LSTM), and verify it with RMSE, MAE and APE performance metrics. Experimental results show that Stacking-SCN can more effectively express the time series relationship of household short-term load, has higher prediction accuracy, effectively prevents network over-fitting problems, and improves the accuracy of prediction capabilities.