Design, Simulation & Load Prediction of Resilient Nano-Grid for Coastal Household

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 11

Year of Publication : 2025

Authors : Vighnesh Binoy, Shankar Nalinakshan, Akash Kinattinkara, Anudev J

10.14445/23488379/IJEEE-V12I11P101

How to Cite?

Vighnesh Binoy, Shankar Nalinakshan, Akash Kinattinkara, Anudev J, "Design, Simulation & Load Prediction of Resilient Nano-Grid for Coastal Household," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 11, pp. 1-11, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I11P101

Abstract:

As the hazards associated with both natural and artificial threats continue to increase, nano-grids have become increasingly important as a crucial component for guaranteeing the continuous supply of energy. Nano-grids are proving to be an effective method of integrating decentralized renewable energy sources when the utility grid is in operation. This research deals with the design and simulation of a Nano-grid along with the electric load prediction for 24 hours in advance using an ARIMA-XGBoost ensemble model. The model performs exceptionally well when compared to the individual ARIMA and XGBoost models, according to evaluations using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. Values of MAE, RMSE, and MAPE are 260.41, 277.72, and 13.67% respectively. The predictions are employed in the proposed model to regulate the modes of operation of the Nano-grid. Existing research works have not focused on controlling power flow in a Nano grid using an AI-based technique. In the proposed work, a novel ARIMA-XGBoost ensemble algorithm is used for controlling power flow in a Nano-grid. Therefore, this approach contributes to both automation and increased grid resilience.

Keywords:

ARIMA, Hybrid model, Nano-grid, Resiliency, XGBoost.

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