Descriptive Analytics Based Anomaly Detection for Cybersecure Load Forecasting As power delivery systems evolve and become increasingly reliant on accurate forecasts, they will be more vulnerable to cybersecurity issues. A coordinated data attack by sophisticated adversaries can render existing data corrupt or outlier detection methods ineffective. This would have a very negative impact on operational decisions. The focus of this study is to develop descriptive analytics based methods (DABMs) for anomaly detection to protect the load forecasting process against cyberattacks to essential data. We propose an integrated solution (IS) and the hybrid implementation of the IS (HIIS) that can detect and mitigate cyberattack induced long sequence anomalies. The HIIS is also capable of improving true positive rates and reducing false positive rates significantly. The proposed HIIS can serve as an online cybersecure load forecasting scheme.

A Hybrid Method of CascadeFiltering and Committee Decision Mechanism for Non Intrusive Load Monitoring There are, in general, two methods of load monitoring which are intrusive load monitoring and nonintrusive load monitoring (NILM). The NILM method has attracted considerable research interests, since it only requires a set of voltage and current sensors to be installed at the electrical service entry (ESE) for load disaggregation. However, the main challenge of NILM is to accurately analyze data from the ESE and determine the electrical consumption of each appliance. A single load signature (LS) has some limitations and may not be used for the load disaggregation under all circumstances. Thus, the methods such as committee decision mechanism (CDM), which employ multiple LSs for load disaggregation, have been proposed. However, the CDM requires extensive calculations. This paper presents a new NILM method that combines a cascadefiltering approach with the CDM. The proposed method enhances the load identification capability of the NILM. A platform is built to validate the proposed method. The experimental results indicate that the identification accuracy of the proposed method is higher compared with the existing NILM methods. Moreover, the computation burden is much less when compared with the CDM method, demonstrating the high computational efficiency of the proposed method.