Integrating Big Data and Machine Learning for Operational Optimization in Utility Networks

The utility sector is invaded with Big Data and Machine Learning (ML) by enabling the operational, predictive, and real time decision-making. Due to the huge amounts of structured and unstructured data generated from IoT sensors, SCADA systems, smart meters, etc., traditional utility operation cannot handle data management and further analyse and optimize the data. The use of ML in the utilities delivers automated processing, pattern recognition, and predictive analytics which helps to shift utilities from the historically reactionary to the next phase of proactivity. Fault detection and forecasting of demand are supported by the supervised learning methods, anomaly detection and clustering can be done using the unsupervised learning while reinforcement learning optimizes the real time allocation of resources. Also, cloud computing and edge processing increase the scalability to the point where any increasement in data volume is tackled correctly. In this paper, the combined effect of these technologies to advance fault detection, reduce system failures, minimize load balancing, and reduce costs and improve service reliability are described. This finding indicates that ML generated Big Data analytics will enable the utility networks to be smarter and more resilient and at a lower cost.