Power Consumption Predicting and Anomaly Detection Based on Transformer and K-Means

Junfeng Zhang,Song Ding,Xiaoxiong Zhang,Hui Zhang

doi:10.3389/fenrg.2021.779587

Abstract

With the advancement of technology and science, the power system is getting more intelligent and flexible, and the way people use electric energy in their daily lives is changing. Monitoring the condition of electrical energy loads, particularly in the early detection of aberrant loads and behaviors, is critical for power grid maintenance and power theft detection. In this paper, we combine the widely used deep learning model Transformer with the clustering approach K-means to estimate power consumption over time and detect anomalies. The Transformer model is used to forecast the following hour’s power usage, and the K-means clustering method is utilized to optimize the prediction results, finally, the anomalies is detected by comparing the predicted value and the test value. On real hourly electric energy consumption data, we test the proposed model, and the results show that our method outperforms the most commonly used LSTM time series model.

Highlights

Modern power systems are evolving in a more sustainable path
We propose in this paper that we utilize the transformer model to estimate electric energy load, apply the k-means approach to further improve the prediction results, and compare the prediction results to the test data to look for anomalies
The prediction of electric energy consumption and the identification of anomalies are critical in the functioning of the power grid, and the processing of multi-variable time series is a difficult challenge

Summary

Introduction

Modern power systems are evolving in a more sustainable path. The load demand for domestic electrical energy is gradually increasing as the number of household appliances and electric cars increases. The power system has grown in complexity and intelligence, and more modern information transmission technology has been implemented, making grid processing more convenient and secure (Bayindir et al, 2016). Electric energy consumption in everyday living is difficult and variable. For example, may vary significantly depending on the season, and consumption on working days and working days will fluctuate. There will be anomalies in the electrical load, such as forgetting to turn off electrical appliances, failure of electrical appliances and even the theft of electricity, and so on, resulting in a much larger electrical demand than typical.

Methods

Results

Conclusion