Electricity Demand Forecasting & Grid Planning for India

Abstract

Accurate electricity demand forecasting plays a crucial role in efficient power system operation, grid stability, and long term infrastructure planning. This study proposes a data driven approach for electricity demand forecasting and grid planning for India using statistical and machine learning techniques. Historical electricity consumption data along with temporal features such as seasonal patterns and time based indicators are analyzed to identify demand trends. The proposed framework integrates multiple forecasting models, including SARIMA, Prophet, and Random Forest Regressor, to capture both linear seasonal patterns and complex non linear relationships in electricity demand. Feature engineering techniques such as lag features and time based variables are employed to improve prediction performance. The models are evaluated using standard error metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). Experimental results demonstrate that machine learning models, particularly Random Forest, provide improved forecasting accuracy compared to traditional statistical methods. The forecasted demand is further analyzed to support grid planning decisions, including peak load identification and infrastructure expansion strategies. The proposed system provides a scalable solution that can assist energy planners and policymakers in improving electricity distribution efficiency and supporting future smart grid development.

Introduction

Electricity is vital for economic growth and industrial development, and accurate demand forecasting is critical for efficient power generation, load balancing, and grid stability. Demand is influenced by seasonal variations, weather, economic activity, and population growth, making traditional linear models insufficient for accurate predictions.

Objective:
The study proposes an integrated forecasting framework using SARIMA, Prophet, and Random Forest Regressor to improve electricity demand prediction for better grid planning.

Methodology:

1. Data Collection & Preprocessing: Historical electricity consumption data is collected, cleaned, and formatted; missing values are handled.

2. Feature Engineering: Time-based features (year, month, day, hour) and lag features are generated to capture patterns in past demand.

3. Model Implementation:

   • SARIMA – captures seasonal trends in electricity demand.

   • Prophet – handles trend, seasonality, and irregular patterns.

   • Random Forest Regressor – ensemble model capturing complex nonlinear relationships.

4. Model Evaluation: Performance is measured using MAE, RMSE, and MAPE to select the most accurate forecasting model.

System Architecture:
The system follows a structured workflow with data acquisition, preprocessing, feature engineering, model training, evaluation, and visualization. It ensures scalability, modularity, and reliable predictions for grid planning.

Experimental Results:

• Random Forest Regressor outperformed SARIMA and Prophet in forecasting accuracy due to its ability to model nonlinear patterns.

• The approach demonstrates that machine learning significantly enhances demand prediction, providing reliable insights for energy management.

Future Scope:

• Integrate additional variables like weather, economic indicators, and population growth.

• Implement advanced deep learning models (LSTM, GRU) for long-term temporal dependencies.

• Enable real-time forecasting using IoT and smart meter data.

• Cloud deployment and integration with smart grids to support policymakers and utilities in power generation and renewable energy planning.

Dataset:
Time series electricity consumption data with timestamps, preprocessed to remove inconsistencies, and enriched with temporal and lag features. Training and testing sets are used to evaluate model performance.

Conclusion

This study presented a machine learning based approach for electricity demand forecasting and grid planning for India. The system analyzes historical electricity consumption data and applies forecasting models such as SARIMA, Prophet, and Random Forest to predict future electricity demand. The experimental results show that the Random Forest model provides better prediction accuracy compared to traditional statistical models. The proposed system can help power utilities and energy planners improve grid stability, energy management, and infrastructure planning.

References

[1] H. S. Hippert, C. E. Pedreira, and R. C. Souza, Neural Networks for Short Term Load Forecasting, IEEE Transactions on Power Systems, 2001. ????https://ieeexplore.ieee.org/abstract/document/910780 [2] S. J. Taylor and B. Letham, Forecasting at Scale, The American Statistician, 2018. ???? https://www.tandfonline.com/doi/abs/10.1080/00031305.2017.1380080 [3] T. Hong and S. Fan, Probabilistic Electric Load Forecasting: A Tutorial Review, International Journal of Forecasting, 2016. ???? https://www.sciencedirect.com/science/article/abs/pii/S0169207015001508 [4] L. Breiman, Random Forests, Machine Learning, 2001. ???? https://link.springer.com/article/10.1023/a:1010933404324 [5] R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 2018. ???? https://books.google.co.in/books?hl=en&lr=&id=_bBhDwAAQBAJ&oi=fnd&pg=PA7&dq=Forecasting+Principles+and+Practice+Hyndman&ots=TjlYwlVLIK&sig=POfbcROiBfhI4_4pwnbJQSCtzug&redir_esc=y - v=onepage&q=Forecasting%20Principles%20and%20Practice%20Hyndman&f=false [6] G. Dudek, Short Term Load Forecasting Using Random Forests, 2015. ???? https://link.springer.com/chapter/10.1007/978-3-319-11310-4_71

Copyright

Copyright © 2026 D. Yasovardhan Reddy, M. Keerthi, M. Suchitra, M. Reddy Sasidhar, B. Likitha, M. C. Leelu Mamakarni. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.