Open AccessElectricity Prediction under Edge Devices Based on Sparse Anomaly Perception
Author(s) -
Jun Yang,
Aidong Xu,
Yiming Zeng,
Li Cheng Li,
Yixin Jiang,
Yunan Zhang,
Hong Wen
Publication year - 2020
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 - 10.1088/1742-6596/1659/1/012015
Subject(s) - electricity , computer science , enhanced data rates for gsm evolution , artificial intelligence , energy (signal processing) , field (mathematics) , machine learning , perception , anomaly detection , data science , engineering , electrical engineering , mathematics , statistics , neuroscience , pure mathematics , biology
Energy issues are closely related to human development. With the changes of the times and the rapid development of technology, power energy has become one of the indispensable energy in human social life, and is the most important part of energy field in modern society. Electricity prediction, as the basis for power system operation, optimization, and control, is facing new challenges in today’s rapidly evolving energy system environment. A large number of machine learning technology and deep learning technology have been applied to electricity prediction and achieved good results. In the edge computing environment, anomalous data collection is characterized by sparsity and time window, and machine learning regression algorithms are often affected by anomalous data. Electricity prediction under edge devices based on sparse anomaly perception is proposed, which combines the drop out idea and subsample idea to alleviate the above problems to some extent. And it can achieve faster training and more accurate prediction.