Open AccessPower Consumption Prediction Based on Deep Learning
Author(s) -
Xuanwen Zhang,
Li Liu
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1325/1/012207
Subject(s) - consumption (sociology) , computer science , deep learning , power consumption , artificial intelligence , electricity , technology forecasting , power (physics) , demand forecasting , machine learning , operations research , engineering , electrical engineering , social science , physics , quantum mechanics , sociology
The purpose of this paper is to select a power consumption forecasting method with high accuracy and low error. Previous power consumption forecasting methods are basically based on the optimization and improvement of the original classical forecasting methods, and the error has not been substantially reduced. It was not until 2006 that the unveiling of deep learning technology opened a new chapter in the direction of artificial intelligence, and the research of power consumption forecasting has ushered in a new wave. In this paper, the origin of deep learning technology is introduced, and the LSTMs model of deep learning is built, and the short-term electricity consumption forecasting model is built, which can complete the forecasting of the time series of electricity consumption. At the end of this paper, a case simulation analysis is carried out. After 57 days’training of power consumption data, the power consumption curve in the next week is finally obtained. It is found that the error rate is very small and the accuracy is high.