Optimizing Home Energy Usage: HEMS-IoT Integration with Big Data and Machine Learning

Ravikiran Mahadasa

doi:10.18034/apjee.v9i1.731

Authors

Ravikiran Mahadasa Senior ETL Developer, Data Inc. (State of Lousiana), 4634 Monument Valley Dr, Indian Land, SC 29707, USA

DOI:

https://doi.org/10.18034/apjee.v9i1.731

Keywords:

Home Energy Management System (HEMS), Internet of Things (IoT), Big Data Analytics, Machine Learning, Energy Optimization, Smart Home Technology, Sustainable Living

Abstract

The goal of this project is to optimize household energy consumption by combining machine learning (ML), big data analytics, and the Internet of Things (IoT) with household Energy Management Systems (HEMS). The primary goals are to assess how well HEMS-IoT integration contributes to cost savings, environmental sustainability, and energy efficiency in residential contexts. The methodology includes a thorough analysis of current literature, real-world case studies, and experimental results to examine the advantages, restrictions, and policy implications of HEMS-IoT integration. Among the key findings are personalized energy management, cost savings, increased energy efficiency, and home behavioral changes. Policy implications emphasize how crucial it is to address issues with fairness, data privacy, accessibility, and interoperability through proactive regulatory frameworks and policy interventions. The study highlights how HEMS-IoT integration can revolutionize residential energy efficiency and move us closer to a more robust and sustainable energy ecosystem.

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