期刊
ENERGY CONVERSION AND MANAGEMENT
卷 49, 期 5, 页码 1156-1166出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2007.09.009
关键词
load modelling; short term load forecasting; neural networks; Bayesian inference; model selection
Short term load forecasting (STLF) is an essential tool for efficient power system planning and operation. We propose in this paper the use of Bayesian techniques in order to design an optimal neural network based model for electric load forecasting. The Bayesian approach to modelling offers significant advantages over classical neural network (NN) learning methods. Among others, one can cite the automatic tuning of regularization coefficients, the selection of the most important input variables, the derivation of an uncertainty interval on the model output and the possibility to perform a comparison of different models and, therefore, select the optimal model. The proposed approach is applied to real load data. (C) 2007 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据