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Real-time anomaly detection for very short-term load forecasting
2018
Journal of Modern Power Systems and Clean Energy
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
doi:10.1007/s40565-017-0351-7
fatcat:ksdpv6n2cbbgzanlbnv7h6zfpa