Data stream mining techniques for real-time monitoring and control of smart power grids in Kenya: challenges and opportunities

Abstract

Kenya’s increasingly evolving power grid requires advanced data stream mining techniques for more effective real-time monitoring and control. This paper examines how these techniques are currently being used in Kenya’s smart power networks, highlighting both their advantages and disadvantages. Although the current systems support basic monitoring and fault detection, they are unable to effectively handle high data velocity, variability, and the integration of renewable energy. High prices, weak regulatory support, limited technical skills, poor data quality, and inadequate computational infrastructure are some of the main obstacles. This paper identifies machine learning, adaptive clustering, and edge computing as possible solutions for improving fault detection, dynamic load balancing, real-time monitoring, and the integration of renewable energy. These strategies optimize grid performance while addressing latency and scalability challenges. The study recommends implementing advanced data stream mining techniques, making significant infrastructural investments, boosting technical capacity, and creating supportive legislative frameworks in order to improve Kenya’s smart power grid. Implementing these changes may help Kenya to enhance its grid resilience and reliability, boost energy efficiency, and promote long-term sustainability.

Clinical trial registration: Not applicable.