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@article{176402,
        author = {Abdul Rahman M and Dr. Prakash S and Tharun Prasath M and Sejin R},
        title = {Optimizing Household Electricity With Linear Regression-Based Prediction And Fuzzy Appliance Matching},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5198-5201},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176402},
        abstract = {This invention presents an intelligent electricity management system designed to optimize household energy consumption using linear regression-based prediction and fuzzy appliance matching. By collecting detailed data on home appliances—such as power rating, usage duration, and age—the system leverages machine learning to accurately forecast monthly electricity usage for each device. High-consumption appliances are identified and matched with energy-efficient alternatives through fuzzy string matching techniques, ensuring personalized and practical recommendations. Visual analytics further enhance user awareness by illustrating consumption trends and potential savings. The system promotes sustainable energy practices, reduces electricity bills, and supports smart home integration for future-ready environmental solutions.},
        keywords = {Energy Management, Linear Regression, Household Electricity Prediction, Fuzzy Matching, Appliance Recommendation, Smart Home, Machine Learning, Energy Efficiency, Data Analytics, Sustainability, K-Means Clustering, Power Consumption Optimization, Electricity Usage Forecasting, Environmental Impact Reduction.},
        month = {April},
        }