Analysis of GHI Forecasting Using Seasonal ARIMA
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
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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. Keywords Solar energy · GHI · Time series · ARIMA
A. Kumar Barik (B) · S. Malakar · A. Chakrabarti A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, India e-mail: [email protected] S. Goswami Bangabasi Morning College, Kolkata, India B. Ganguli · S. Sen Roy Department of Statistics, University of Calcutta, Kolkata, India © Springer Nature Singapore Pte Ltd. 2021 N. Sharma et al. (eds.), Data Management, Analytics and Innovation, Advances in Intelligent Systems and Computing 1175, https://doi.org/10.1007/978-981-15-5619-7_5
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1 Introduction The conventional energy sources are being used for a long period of time and hence their reserves are about to end. These sources of energy have a lot of advantages like availability, easy productivity. But there are also a number of drawbacks and one of them is it is extensively harmful to our nature which has more impact than its advantages. On the other hand, the unconventional sources of energy have no end. These renewable sources of energy leave no pollutant to the environment, which is directly related to global warming. Solar energy is one of the most important renewable energy sources. Solar energy is derived directly from the sun in the form of radiation [11]. To use solar energy with conventional energy, prediction of solar energy is important for various reasons like planning of operation, energy delivery, use of storage, grid integration, etc. Solar radiation prediction is necessary as it has been identified as one of the most impor
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