Real-time Anomaly Detection for Very Short-term Load Forecasting

Many very short-term load forecasting (VSTLF) models in literature rely on lagged loads, while most of these VSTLF papers assume perfect information of the lagged loads. As a result, the accuracy reported in the VSTLF literature has been amazingly high. In reality, however, load forecasters may not have access to the load values of the most recent few hours. The imperfection of the recent load information would certainly affect the load forecast accuracy. This paper tackles a practical problem, how to detect the anomalies in the most recent load information.

Citation
Jian Luo, Tao Hong and Meng Yue, "Real-time anomaly detection for very short-term load forecasting," Journal of Modern Power Systems and Clean Energy, in press, available online. (open access)

Real-time Anomaly Detection for Very Short-term Load Forecasting

Jian Luo, Tao Hong and Meng Yue

Abstract

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonly used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Finally, a general anomaly detection framework is proposed for the future research.