Integrating Machine Learning Techniques for Enhanced Energy Management and Sustainability in Smart Homes
DOI:
https://doi.org/10.54489/93g31089Keywords:
Smart home, machine learning, energy management, smart cityAbstract
As energy consumption continues to rise due to technological advancements and the increasing adoption of electric vehicles, the need for efficient energy management in smart homes has never been more critical. This paper presents an intelligent approach to enhancing energy management and sustainability in smart homes through the integration of advanced machine-learning techniques. By analyzing resident behavior and energy usage patterns, the proposed Smart Home Energy Management System (SHEMS) optimizes the operation of home appliances to reduce energy consumption while maintaining comfort. The system leverages machine learning algorithms to predict energy demand, adapt to changing conditions, and provide personalized energy-saving recommendations. This approach not only enhances energy efficiency but also contributes to the reduction of carbon emissions, aligning with global efforts to mitigate climate change. The paper outlines the implementation of the machine learning models, detailing their integration within the smart home ecosystem, and demonstrates the system's effectiveness in achieving sustainable energy management. Through this innovative solution, smart homes can play a pivotal role in fostering a more sustainable and energy-efficient future.
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