Intelligent Electricity Forecasting and Resource Optimization for Smart Grid Efficiency

K Siva Krishna; Annapureddy Sanjana; Kancharla Jenny; Vijayanagaram Tarun Sai Manikanta; Medikonda Vamsi

Intelligent Electricity Forecasting and Resource Optimization for Smart Grid Efficiency

Author(s)	Dr. K Siva Krishna, Ms. Annapureddy Sanjana, Ms. Kancharla Jenny, Mr. Vijayanagaram Tarun Sai Manikanta, Mr. Medikonda Vamsi
Country	India
Abstract	Rapid urbanization, climate variability, and evolving consumption patterns have significantly increased the complexity of electricity demand management, posing challenges to efficient power distribution and grid stability. Accurate prediction of electricity demand is difficult due to the dynamic and non-linear relationships between influencing factors such as weather conditions, temporal variations, and consumption behavior. This paper proposes an AI-driven electricity load segregation and demand–supply optimization framework for smart power grids. The proposed system integrates historical electricity consumption data with meteorological and temporal parameters to model complex demand behavior. A machine learning approach based on Random Forest Regression is employed for accurate load forecasting, while intelligent load segregation enables effective allocation of power resources across residential, commercial, and industrial sectors. Data preprocessing techniques such as normalization, feature selection, and cross-validation are applied to enhance model robustness. The system’s performance is evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Square Error (MSE), and R² score. Experimental results demonstrate that the proposed AI-based approach significantly improves prediction accuracy and reduces forecasting errors compared to traditional methods. The system enhances power procurement planning, reduces operational costs, and improves overall grid reliability. This work highlights the potential of artificial intelligence in achieving sustainable and efficient energy management in smart grid environments.
Keywords	Electricity Load Segregation, Demand–Supply Optimization, Artificial Intelligence, Machine Learning, Random Forest Regression, Smart Grid, Power Demand Forecasting, Energy Management Systems, Grid Stability, Meteorological Data Integration
Field	Computer > Data / Information
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-30

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Intelligent Electricity Forecasting and Resource Optimization for Smart Grid Efficiency

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