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@article{177660,
        author = {Nair Sivabalkrishnan and Rahul singh and Rajeev Nair},
        title = {Renewable Energy Forecasting},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {1389-1393},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177660},
        abstract = {As the global energy sector pivots toward sustainability, driven by growing concerns over climate change and carbon emissions, renewable energy has emerged as a cornerstone of future energy policy. Yet, the integration of solar, wind, and hydro power into traditional electricity grids introduces significant challenges due to their dependency on fluctuating meteorological conditions. Ensuring consistent grid stability while accommodating these intermittent sources necessitates accurate, data-driven forecasting systems.
This research presents a comprehensive dual-model forecasting framework aimed at predicting both renewable energy production and daily electricity load demand in the state of Maharashtra, India. The methodology leverages the Random Forest Regressor—an ensemble learning algorithm capable of managing non-linear relationships and high-dimensional datasets. The first model forecasts total renewable energy generation from wind, solar, and hydro sources. The second model predicts daily electric load based on historical weather patterns and consumption data.
Extensive data preprocessing, intelligent feature engineering, and visual exploration techniques were applied to develop high-performing models. The renewable energy forecasting model yielded an R² score of 0.94, indicating strong correlation between predicted and actual values, while the electric load model achieved an R² of 0.82. Further, an energy balance analysis highlighted approximately 210 surplus days and 155 deficit days during 2024, offering actionable intelligence for grid operators.
This unified forecasting framework demonstrates not only the feasibility but also the critical importance of predictive analytics in renewable energy grid management. It facilitates smarter operational strategies, load-shifting mechanisms, and optimized use of storage systems, thereby ensuring sustainable, resilient, and cost-effective power delivery.},
        keywords = {},
        month = {May},
        }