Abstract
A precise understanding of solar energy generation is important for many reasons like storage, delivery, and integration. Global Horizontal Irradiance (GHI) is the strongest predictor of actual generation. Hence, the solar energy prediction problem can be attempted by predicting GHI. Auto-Regressive Integrated Moving Average (ARIMA) is one of the fundamental models for time series prediction. India is a country with significant solar energy possibilities and with extremely high weather variability across climatic zones. However, rigorous study over different climatic zones seems to be lacking from the literature study. In this paper, 90 solar stations have been considered from the 5 different climatic zones of India and an ARIMA model has been used for prediction for the month of August, the month with most variability in GHI. The prediction of the models has also been analyzed in terms of Root Mean Square Error. The components of the AR models have also been investigated critically for all climatic zones. In this study, some issues were observed for the ARIMA model where the model is not being able to predict the seasonality that is present in the data. Hence, a Seasonal ARIMA (SARIMA) model has also been used as it is more capable in case of seasonal data and the GHI data exhibits a strong seasonality pattern due to its availability only in the day time. Lastly, a comparison has also been done between the two models in terms of RMSE and 7 days Ahead Prediction.
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Acknowledgments
This article results from the INDO-USA collaborated project, named, LISA 2020 on Renewable Energy and joint collaboration with the University of Colorado, which was funded by The United States Agency for International Development (USAID) and the research has been carried out in the Data Science Laboratory, University of Calcutta supported by TEQIP Phase 3.
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Kumar Barik, A., Malakar, S., Goswami, S., Ganguli, B., Sen Roy, S., Chakrabarti, A. (2021). Analysis of GHI Forecasting Using Seasonal ARIMA. In: Sharma, N., Chakrabarti, A., Balas, V.E., Martinovic, J. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1175. Springer, Singapore. https://doi.org/10.1007/978-981-15-5619-7_5
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