SMART HOME ENERGY SAVING WITH BIG DATA AND MACHINE LEARNING - Ahmad, Hawar Bahzad; Asaad, Renas Rajab; Almufti, Saman M; Hani, Ahmed Alaa; Sallow, Amira Bibo; Zeebaree, Subhi R. M. (Jurnal Ilmiah Ilmu Terapan Universitas Jambi, 2024)
In response to escalating energy consumption, particularly within the housing sector, a global imperative to reduce energy usage has emerged, propelling the concept of "smart houses" to the forefront of innovation. This paradigm shift owes its genesis to the convergence of advancements in energy conversion, communication networks, and information technology, catalyzing the emergence of the Internet of Things (IoT). The IoT facilitates seamless connectivity of devices via the World Wide Web, enabling remote management, monitoring, and detection capabilities. Capitalizing on this technological synergy, the integration of IoT, big data, and machine learning with home automation systems holds immense promise for enhancing energy efficiency. This paper introduces HEMS-IoT, a groundbreaking energy control system for intelligent homes, underpinned by big data analytics and machine learning algorithms, prioritizing security, convenience, and energy conservation. Leveraging J48 neural network technology and the Weka API, the study illuminates user behaviors and energy consumption patterns, enabling household classification based on energy usage profiles. Moreover, to ensure user comfort and safety, RuleML and Apache Mahout are deployed to customize energy-saving recommendations tailored to individual preferences. By presenting a practical demonstration of smart home monitoring, this paper validates the effectiveness of the proposed approach in enhancing security, comfort, and energy conservation. This pioneering research not only showcases the transformative potential of IoT-driven energy management systems but also sets the stage for a sustainable and interconnected future.