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.

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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
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Comparative Analysis of ARIMA and SARIMA Models for Improving Solar Power Prediction Accuracy in Bangalore Region

Binod Kumar Sahu, Bhabasis Mohapatra, Ritesh Dash, Adithya Ballaji et al.
1 citations
Energy Load and Power Forecasting
0.73
article

Comparative Analysis of ARIMA and SARIMA Models for Improving Solar Power Prediction Accuracy in Bangalore Region

Binod Kumar Sahu, Bhabasis Mohapatra, Ritesh Dash, Adithya Ballaji, S N Pallavi
article en
1 citations

Abstract

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.

Siksha O Anusandhan University (IN), REVA University (IN)
Affordable and clean energy
Openalex Percentile: Top 26%
Energy Load and Power Forecasting
0.73
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Comparative Analysis of ARIMA and SARIMA Models for Improving Solar Power Prediction Accuracy in Bangalore Region — Binod Kumar Sahu, Bhabasis Mohapatra, et al. · (2025) | TGRS Research Map | TGRS