Energy Price Prediction on the Romanian Market using Long Short-Term Memory Networks

被引：0
|
作者：
Ioanes, Andrei [1 ]
Tirnovan, Radu [1 ]
机构：
[1] Tech Univ Cluj Napoca, Fac Elect Engn, Cluj Napoca, Romania
来源：
2019 54TH INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE (UPEC) | 2019年
关键词：
Power Grid; Demand Curve; Machine Learning; Long Short-Term Memory Algorithm;
D O I：
暂无
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Transition to a market-based economy has reached, eventually, the production of electrical energy in Romania. Historically considered a never-ending resource, the producers did not have to interact with the consumer and more specific with the demands of the consumers. This paper proposes a neural algorithm based on Long Short-Term Memory (LSTM) architecture able to assess the price of energy as a time sequence application and predict trends based on the interaction between resources availability and demand.
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页数：5
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