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Error Distribution-based Anomaly Score for Forecasting-based Anomaly Detection of PV Systems

For forecasting-based anomaly detection for PV systems, in this paper, we propose a way for calculating anomaly scores. The basic idea of our approach is to utilize the distribution of forecasting errors of normal data to derive relative anomaly score, which is limited to from 0 to 100. To further improve the anomaly score, we apply our basic idea to each month separately because the distribution of forecasting errors changes over time. Through experiments using the real data, we examine some aspects of our approach preliminarily.