Comparative Analysis of ARIMA and SARIMA Models for Improving Solar Power Prediction Accuracy in Bangalore Region
Accurate solar power forecasting is crucial for efficient grid management and sustainable energy usage. This study presents a comparative analysis of ARIMA and SARIMA models using solar irradiance data from Bangalore (January 2023 - August 2024). Model accuracy was evaluated using RMSE, MAPE, AIC, and BIC. Results indicate that SARIMA outperforms ARIMA with a 9.87 % MAPE (vs. 11.52 % in ARIMA) and an RMSE of 0.8651 (vs. 0.9123 in ARIMA). The findings highlight SARIMA's effectiveness in capturing seasonal patterns, making it a superior choice for solar energy forecasting in regions with significant seasonal variations. These insights aid energy planners in optimizing solar power integration into the grid.
Authors
- Binod Kumar Sahu (ORCID: https://orcid.org/0000-0001-5639-9928)
- Bhabasis Mohapatra (ORCID: https://orcid.org/0000-0002-7164-6437)
- Ritesh Dash (ORCID: https://orcid.org/0000-0001-8731-603X)
- Adithya Ballaji (ORCID: https://orcid.org/0000-0003-0432-7142)
- S N Pallavi
Institutions
- Siksha O Anusandhan University (IN)
- REVA University (IN)
Publication Details
- Published
- 2025-05-23
- DOI
- https://doi.org/10.1109/apsit63993.2025.11086259
- Citations
- 1
- Primary Topic
- Energy Load and Power Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 0.73