Application of a hybrid quantized Elman neural network in short-term load forecasting

Abstract

This paper investigates the short-term load forecasting (STLF) problem via a hybrid quantized Elman neural network (HQENN) with the least number of quantized inputs, hourly historical load, hourly predicted target temperature and time index. The purpose is to show the capabilities of HQENN to learn the complex dynamics of hourly power load time series and forecast the near future loads with high accuracies. The HQENN model is comprised of the qubit neurons and the classic neurons. The laws of quantum physics are employed to describe the interactions of the qubit neurons and the classic neurons. The extended quantum learning algorithm makes the context-layer weights being extended into the hidden-layer weights matrix such that they can be updated along with hidden-layer weights to extract more information about the load series. To improve the forecasting accuracy, the genetic algorithm (GA) is introduced to obtain the optimal or suboptimal structure of the HQENN model. The results indicate that the forecasting method based on HQENN has an acceptable high accuracy.