长短期记忆网络在天然气需求预测中的应用

Athanasios Anagnostis, E. Papageorgiou, Vasileios Dafopoulos, D. Bochtis
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引用次数: 11

摘要

长短期记忆(LSTM)算法包含了先进的递归神经网络方法的特点,在本研究中用于预测希腊短期天然气需求。LSTM被研究人员普遍认为是时间序列预测问题的关键工具,并在过去的几年里在许多不同的科学领域发现了重要的适用性。在本研究中,我们将提出的LSTM应用于希腊天然气电网的三个分配点(城市)的一天前天然气需求预测。通过不同的人工神经网络(ANN)结构进行了比较分析,结果更深入地了解了大型城市中心的特征,表明了所提出的方法在预测天然气日常需求方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Applying Long Short-Term Memory Networks for natural gas demand prediction
Long Short-Term Memory (LSTM) algorithm encloses the characteristics of the advanced recurrent neural network methods and is used in this research study to forecast the natural gas demand in Greece in the short-term. LSTM is generally recognized by researchers as a key tool for time series prediction problems and has found important applicability in many different scientific domains over the last years. In this study, we apply the proposed LSTM for the purposes of a day-ahead natural gas demand prediction to three distribution points (cities) of Greece’s natural gas grid. A comparative analysis was conducted by different Artificial Neural Network (ANN) structures and the results offer a deeper understanding of the large urban centers characteristics, showing the efficacy of the proposed methodology on predicting natural gas demand in a daily basis.
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