Using Advanced Audio Generating Techniques to Model Electrical Energy Load

被引：0

作者：

Farkas, Michal ^{[1
]}

Lacko, Peter ^{[1
]}

机构：

[1] Slovak Univ Technol Bratislava, Fac Informat & Informat Technol, Bratislava, Slovakia

来源：

ENGINEERING APPLICATIONS OF NEURAL NETWORKS, EANN 2017 | 2017年 / 744卷

关键词：

Artificial neural networks; Deep learning; Time series prediction; WAVELET TRANSFORM; PREDICTION;

D O I：

10.1007/978-3-319-65172-9_4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The prediction of electricity consumption has become an important part of managing the smart grid. Smart grid management involves energy production (from traditional and renewable sources), transportation and measurements (smart meters). Storing large amounts of electrical energy is not possible, therefore it is necessary to precisely predict energy consumption. Nowadays deep learning approaches are successfully used in different artificial intelligence areas. Deep neural network architecture called WaveNet was designed for text to speech task, improving speech quality over currently used approaches. In this paper, we present modification of the WaveNet architecture from speech (sound waves) generation to energy load prediction.

引用

页码：39 / 48

页数：10

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