Using IoT and Machine Learning for Enhanced Home Energy Management in Somalia

Abstract - This paper presents a novel approach to home energy management in Somalia by integrating Internet of Things (IoT)technology with machine learning algorithms to optimize energy consumption in residential settings. The proposed system, Optimizing Home Energy Management in Somalia with IoT Technology, utilizes real-time data analytics to manage the operation of home appliances efficiently. By leveraging data on electricity price fluctuations and renewable energy availability, the system intelligently determines the most cost-effective and sustainable energy sources to utilize at any given time. It provides
recommendations on which appliances to operate or turn off to minimize energy use and costs. The methodology involves the
deployment of IoT sensors and devices across various home appliances to monitor and control energy consumption dynamically.
Machine learning algorithms analyze patterns in energy usage and predict future trends to optimize the scheduling of appliance
operations, thus enhancing energy efficiency and reducing dependency on non-renewable energy sources. The study’s findings
demonstrate significant improvements in energy management, highlighting reductions in energy consumption and costs and
contributing to environmental sustainability. This research contributes to the growing field of smart energy solutions in
developing countries, offering a scalable model that can be adapted to different regional settings beyond Somalia.
Keywords - Home energy management, Renewable energy, Energy efficiency, Machine Learning, Internet of Things.