Machine learning methods have been prevailing in wind power forecasting, while these data-
driven based methods are susceptible to cyberattacks. Typical attack methods inject …

Demand side management (DSM) contributes to the industry's transition to renewables by
shifting electricity consumption in time while maintaining feasible operations. Machine …

Deep learning (DL) has emerged as a key technique in smart grid operations for task
classification of power quality disturbances (PQDs). Even though these models have …

State estimation techniques that utilize machine learning are gaining popularity in power
distribution networks with high penetration of distributed energy resources due to their …

Multi-sensor ML models for EO aim to enhance prediction accuracy by integrating data from
various sources. However, the presence of missing data poses a significant challenge …

Earth observation (EO) applications involving complex and heterogeneous data sources are
commonly approached with machine learning models. However, there is a common …

The increasing complexity of electric power grids, owing to the integration of Distributed
Energy Resources (DER), electric vehicles, energy storage systems, and advanced …

Deep learning has gained prominence as an effective approach for enhancing the efficiency
of various applications including smart grids. Although these models excel significantly in …

Earth observation (EO) applications involving complex and heterogeneous data sources are
commonly approached with machine learning models. However, there is a common …