SMART HOME MANAGEMENT USING MACHINE LEARNING

Registration ID: IJNRD_311201

Published ID: IJNRD2512155

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T BLESSING KARUNYA, M UMARAJALAKSHMI, JAFFAR ALI AKBAR ALI, SUDHA SENTHIL KUMAR

Smart Home Management, Home Energy Management , RNN , Load Scheduling , Anomaly Detection , Optimization , Reinforcement Learning , IoT.

Smart homes generate vast amounts of sensor and appliance data that enable automated management for comfort, cost savings, and sustainability. This work reframes classic Home Energy Management System (HEMS) prognosis research into an integrated Smart Home Management (SHM) solution that combines: (1) multivariate energy prognosis using Recurrent Neural Networks (RNN) networks; (2) anomaly detection via Deep Residual Networks; and (3) the automated load-scheduling layer (optimization and rule-based / RL options). We employ a high-resolution smart-home dataset with 1-minute intervals over ~350 days, enriched with weather features and simulated occupancy and tariff signals. The RNN multivariate prognosis module demonstrates strong predictive performance (reported baseline in the dataset: MSE: 0.02284, RMSE: 0.15113, MAE: 0.123, R² = 0.694) to enable robust decision-making for scheduling and cost reduction. We illustrate the SHM pipeline on simulations showcasing peak shaving, reduced electricity cost under time-of-use tariffs, and reliable anomaly detection. The proposed system is designed for hybrid edge-cloud deployment, privacy-aware updates, and user-centric overrides. We utilize a high-resolution smart home dataset of about 340 days with 2-minute sampling intervals, enriched with weather attributes, synthetic occupancy signals, and tariff-based cost indicators. The performance of the multivariate RNN model is exceptionally good, as reflected in the achieved MSE 0.02284 and RMSE 0.15113, thus making it appropriate for real-time decision support. The SHM pipeline demonstrates its potential through various simulations that present its ability to enable cost minimization under time-of-use tariffs, deterministic and learning-based appliance scheduling, peak shaving, and reliable detection of abnormal household energy events. The hybrid edge-cloud architecture ensures low latency, enhanced privacy, and enables scalable deployment in modern smart residential infrastructures.

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T BLESSING KARUNYA, M UMARAJALAKSHMI, JAFFAR ALI AKBAR ALI, & SUDHA SENTHIL KUMAR (December-2025). SMART HOME MANAGEMENT USING MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(12), b500-b510. https://ijnrd.org/papers/IJNRD2512155.pdf

Volume 10 Issue 12, December-2025

Pages : b500-b510

Paper Reg. ID: IJNRD_311201

Published Paper Id: IJNRD2512155

Research Area: Other area not in list

Author Type: Indian Author

Country: Namakal, Tamil Nadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2512155.pdf

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