文章基本信息

标题：A Gaussian-Gaussian-Restricted-Boltzmann-Machine-based Deep Neural Network Technique for Photovoltaic System Generation Forecasting
本地全文：下载
作者：Shota Ogawa ; Hiroyuki Mori
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：4
页码：87-92
DOI：10.1016/j.ifacol.2019.08.160
语种：English
出版社：Elsevier
摘要：AbstractThis paper proposes a new Gaussian-Gaussian-Restricted-Boltzmann-Machine-based method for forecasting photovoltaic (PV) system generation forecasting. Although renewable energy such as PV system and wind power generation has been used to suppress greenhouse gases in the world, it has a drawback that weather conditions influence the generation output significantly. Thus, it is not easy to perform Economic Load Dispatch (ELD) and Unit Commitment in power systems smoothly. From a standpoint of power system operation, more accurate predication models are required to deal with predicted values of PV system generation. In this paper, an efficient Deep Neural Network (DNN) model with Gaussian Gaussian Restricted Boltzmann Machine is presented to predict one-step-ahead PV system generation output. The model is based on Restricted Boltzmann Machine as a feature extractor and MultiLayer Perceptron (MLP) as ANN. The effectiveness of the proposed method is demonstrated for real data of a PV system.
关键词：KeywordsPower systemsForecastingSolar energyTime-series analysisArtificial Intelligence