Application of a hybrid quantized Elman neural network in short-term load forecasting
Application of a hybrid quantized Elman neural network in short-term load forecasting
复制标题
DOI：
10.1016/j.ijepes.2013.10.020
复制
发表时间：
2014-02-01
影响因子：
5.2
通讯作者：
Zhang, Yi
中科院分区：
文献类型：
--
作者：
Li, Penghua;Li, Yinguo;Zhang, Yi
研究了基于混合量化Elman神经网络（HQENN）的短期负荷预测（STLF）问题，该网络具有最少的量化输入、逐时历史负荷、逐时预测目标温度和时间指标。目的是展示HQENN学习小时电力负荷时间序列的复杂动态并以高精度预测近期负荷的能力。HQENN模型由量子位神经元和经典神经元组成。利用量子物理定律描述了量子比特神经元和经典神经元之间的相互作用。扩展量子学习算法将上下文层权值扩展到隐层权值矩阵中，使上下文层权值可以随隐层权值沿着更新，从而提取出更多的负荷序列信息。为了提高预测精度，引入遗传算法（GA）来获得最优或次优的HQENN模型结构。结果表明，基于HQENN的预测方法具有较高的精度。(C)2013爱思唯尔有限公司保留所有权利。
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. (C) 2013 Elsevier Ltd. All rights reserved.