Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 2
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Powering a Sustainable Future: Decadal Trends, Seasonality, and Predictive Forecasting of Wind Energy in Rajasthan

Author(s) Mr. Manish singh chauhan, Dr. Surender Kumar Kulshreshtha
Country India
Abstract This study analyses the temporal dynamics and forecasting behaviour of wind energy generation in Rajasthan using monthly data from April 2015 to January 2026. A comprehensive time-series framework is employed, combining decomposition techniques, trend estimation, stationarity testing, and comparative model evaluation. The results reveal a statistically significant upward trend alongside a strong and stable seasonal pattern driven by monsoon wind regimes. Multiple forecasting models, including ARIMA, ETS, Holt–Winters, TBATS, STLM, STL+RWD, and NNETAR, are evaluated using RMSE, MAE, and MAPE. The findings indicate that the STLM model achieves superior performance in minimizing absolute forecast errors while effectively capturing the underlying seasonal structure. DM test results further show that differences in predictive accuracy across leading models are not statistically significant, suggesting convergence in performance when seasonality is adequately modelled. The 24-month forecasts highlight a consistent intra-annual cycle, with peak generation during monsoon months and lower output during winter. These results underscore the importance of decomposition-based approaches in renewable energy forecasting and provide practical insights for improving seasonal planning, resource allocation, and grid management. By enhancing the predictability of wind energy availability, the study contributes to sustainability-oriented energy planning by supporting more efficient integration of renewable resources, reducing reliance on fossil-fuel-based balancing, and enabling more informed decision-making in the transition toward a low-carbon and resilient energy system.
Keywords Wind Energy Forecasting; STLM; Seasonal Decomposition; Time-Series Analysis; Renewable Energy Planning; Rajasthan; Forecast Accuracy; Sustainability.
Published In Volume 7, Issue 2, March-April 2026
Published On 2026-04-12
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E-ISSN 3048-7641
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