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@article{200095,
        author = {Amit Kumar and Aman Sharma},
        title = {Enhancing Smart Grid Performance Using IoT and Cloud-Based Technologies for Time-Series Data Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1886-1891},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200095},
        abstract = {The modernization of electrical power systems has become essential due to the rapid growth in global energy demand and the increasing integration of renewable energy sources. Traditional grid systems lack the flexibility and intelligence required to manage dynamic energy consumption patterns. This paper presents a comprehensive smart grid framework that integrates Internet of Things (IoT) devices with cloud computing platforms for efficient monitoring, analysis, and forecasting of energy consumption. The system captures real-time data from distributed sensors and processes it using cloud-based analytics. Various time-series forecasting techniques, including statistical and machine learning models, are implemented and compared. Experimental findings reveal that deep learning models, particularly Long Short-Term Memory (LSTM) networks, outperform conventional models in predicting electricity demand. The proposed framework enhances grid reliability, reduces operational inefficiencies, and supports sustainable energy management. The study also discusses practical challenges, implementation considerations, and future research directions.},
        keywords = {Smart Grid, IoT, Cloud Computing, Time-Series Forecasting, Machine Learning, LSTM, Energy Analytics},
        month = {May},
        }