COASTAL EROSION DYNAMICS AND COMMUNITY RESILIENCE IN BAYELSA STATE, NIGERIA: A COMPREHENSIVE TIME SERIES ANALYSIS USING REMOTE SENSING AND GEOSPATIAL TECHNOLOGIES

ABSTRACT Modern electrical grids are becoming increasingly complex due to the integration of renewable energy sources, smart grid technologies, and fluctuating demand patterns. This study presents a comprehensive framework for monitoring grid stability using time series analysis and machine learning-based anomaly detection. A multi-layered approach was developed, combining seasonal-trend decomposition using Loess (STL), ARIMA-based residual analysis, isolation forests, and statistical outlier detection implemented in R programming language. Real-world electricity consumption data from the University of California, Irvine (UCI) Machine Learning Repository, spanning four years (2006–2010), were analyzed to validate the framework. The hybrid anomaly detection system achieved a precision of 92.3% and a recall of 87.6% in identifying consumption anomalies. Statistical decomposition methods detected 156 meaningful anomalies, while machine learning techniques identified 134 anomalies with higher confidence. The framework demonstrates strong potential for real-time grid monitoring applications and, with further operational development, could contribute to reducing unplanned outages.