Power Market Load Forecasting on Neural Network With Beneficial Correlated Regularization （SCI一区，2021年影响因子：9.112）
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影响因子：9.112
DOI码：10.1109/TII.2017.2789297
发表刊物：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
关键字：PORTFOLIO OPTIMIZATION, ELECTRICITY, SYSTEMS; MODELS
摘要：n day-ahead market (DAM), load serving entities (LSEs) are required to submit their future load schedule to market operator. Due to the cost computation, we have found the inconformity between load accuracy and cost of power purchase. It means that more accurate load forecasting model may not lead to a lower cost for LSEs. Accuracy pursuing load forecast model may not target a solution with optimal benefit. Facing this issue, this paper initiates a beneficial correlated regularization (BCR) for neural network(NN) load prediction. The training target of NN contains both accuracy section and power cost section. Also, this paper establishes a virtual neuron and a modified Levenberg-Marquardt algorithm for network training. A numerical study with practical data is presented and the result shows that NN with BCR can reduce power cost with acceptable accuracy level.
备注：SCI一区，2021年影响因子：9.112
合写作者：Xin Cun,Mengxuan Yan,Haoliang Yuan,Loi Lei Lai
第一作者：Fang Yuan Xu
论文类型：期刊论文
通讯作者：Yifei Wang
文献类型：J
卷号：14
期号：11
页面范围：5050-5059
ISSN号：1551-3203
是否译文：否
发表时间：2018-11-01
收录刊物：SCI