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@article{172884,
        author = {Peethala Girija and Nithin Devanaboina and Ramana Nagalakshmi and Valluri Sai Praneetha},
        title = {AI-Based Energy Consumption Prediction and Data-Driven Recommendations for Optimizing Power Usage},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {9},
        pages = {1191-1194},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172884},
        abstract = {This research brings a system with an AI base in order to better predict the usage of energy so that all forms of traditional management of energy might be avoided as much as possible. In these models, a machine learning Random Forest, ARIMA, KNN were employed to make prediction for the use of future energies based on historical data. A web application made using Streamlit is applied, which delivers energy insights real time, to facilitate users' energy utilization improvements. The system maintains a high prediction accuracy, R² = 0.92, and decreases forecasting error by a 5% level over traditional models. Future enhancements will use IoT based smart meters and deep learning to ensure better scalability and precision.},
        keywords = {},
        month = {February},
        }