Using Advanced Audio Generating Techniques to Model Electrical Energy Load
| Autor: | Peter Lacko, Michal Farkas |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Artificial neural network
business.industry Computer science 020209 energy Deep learning Real-time computing Speech synthesis 02 engineering and technology Energy consumption computer.software_genre Renewable energy Smart grid 0202 electrical engineering electronic engineering information engineering Electronic engineering Artificial intelligence Electricity business computer Energy (signal processing) |
| Zdroj: | Engineering Applications of Neural Networks ISBN: 9783319651712 EANN |
| DOI: | 10.1007/978-3-319-65172-9_4 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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