A Method to Inspect the Implementation of Electricity Price Based on Deep Learning Variational Autoencoder
- Resource Type
- Conference
- Authors
- Gao, Xiying; Ye, Ning; Song, Jinchun; Wang, Haomiao; Zhang, Ye; Guan, Yan; Song, Xiaowen; Cai, Yingkai; Zhang, Wenshu; Hui, Qian; Li, Dan
- Source
- 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2) Energy Internet and Energy System Integration (EI2), 2018 2nd IEEE Conference on. :1-5 Oct, 2018
- Subject
- Power, Energy and Industry Applications
Inspection
Anomaly detection
Power supplies
Data models
Decoding
deep learning
variational autoencoder
electricity price inspection implementation
data heterogeneity
- Language
In this paper, we propose a method for performing electricity price execution inspection by using a variational autoencoder technology in deep learning. The variational auto encoder based anomaly detection algorithm(VABAD) can be used both as a discriminant model and as a feature of the generation model, which effectively solves the calculation problem of multiple heterogeneous parameters of current electricity price inspection implementation. The reconstruction probability is a probabilistic measure that takes into account the variability of the distribution of variables. It is used by autoencoder based anomaly detection methods. Experimental results show that the proposed method has been validated and compared to the existing approaches. The databases used in this paper come from Power Marketing System that occurred in Liaoning, China in 2015.