Energy Prediction for Future Energy Supply
DOI:
https://doi.org/10.52783/jns.v14.2996Keywords:
Energy prediction, Smart grids, Object detection, Gated Recurrent Unit (GRU), Energy supply management, Sustainable Energy EnvironmentAbstract
Futrue power load prediction is more important to help avoid energy wastage and evolve efficient power control strategies. Reliable prediction of energy Utilization considered past time series data, which need to be accessed to obtain helpful information and provide future Utilization predictions. With the smart grid and technology measuring development, large interest has generated in energy predicting and its performance in tracking coming energy supply. In specifically, energy forecasting in scenarios with people and objects surrounding the environment is essential for energy utilization maximization. A new energy forecasting framework approach is devised based on a two-stage forecasting procedure to cover this. The first step involved the use of an Improved Singleshot Anchor Box Detector (ISSABD) Algorithm to detect objects and their locations. The algorithm enhances object detection precision and efficiency and allows identification of items that utilize electrical energy. This data is essential in determining energy consumption patterns in the environment. In the second step, an Approach using a deep neural network with Attention-Based-Gated Recurrent Unit (GRU) to predict energy Utilization within a given time period. The GRU model exploits the sequential character of time series data with an attention mechanism to achieve required dependencies and patterns in energy Utilization. Utilizing available Utilization data, this method provides precise estimation of future energy demand to help in the creation of effective energy management policies. Merging object detection and energy Utilization prediction supports decision-making for attaining a sustainable world. Identifying the patterns of energy consumption by various objects and predicting future Utilization, in order to maximize energy distribution, minimize wastage, and ensure energy efficiency. This paper proposes a holistic model for energy prediction that unifies object detection and the GRU model. Reliable prediction of energy Utilization and detection of energy-consuming objects provide useful information and tools for successful energy management across various environments. Reliable and affordable energy can significantly impact people's lives, especially in India. Power load and future energy prediction can lead to better healthcare, education, and economic growth.
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