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@article{172109,
        author = {Vibha d rao and Meghana m and Tejaswini patil and Anirudh s and Suprit d l and Dr. sampath a k and Dr. v joshi manohar},
        title = {Predictive analysis of energy demand prediction using deep learning approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {8},
        pages = {2077-2082},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=172109},
        abstract = {Energy demand prediction is crucial for efficient resource management and planning in rapidly urbanizing cities like Bangalore. This study explores a deep learning-based predictive analysis approach using time-series energy consumption data provided by BESCOM. The study explores and evaluates various models, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), for predicting quarterly energy demand. Key metrics such as RMSE, MAE, and MAPE are used to evaluate predictive accuracy, while qualitative metrics assess the robustness and interpretability of the models. Results demonstrate that hybrid and tailored architectures outperform traditional models, offering improved accuracy and scalability.},
        keywords = {},
        month = {January},
        }