@inproceedings{7aa74b5e88ad4ffdb793599505213591,
title = "Forecasting of total daily solar energy generation using ARIMA: A case study",
abstract = "In this paper, a well known statistical modeling method named ARIMA has been used to forecast the total daily solar energy generated by a solar panel located in a research facility. The beauty of the ARIMA model lies in its simplicity and it can only be applied to stationary time series. So our time series data, which is seasonal and non-stationary, is transformed into a stationary one for applying the ARIMA model. The model is developed using sophisticated statistical techniques. The optimum model is chosen and validated using Akaike information criterion (AIC) and residual sum of squares (SSE). Error analysis is done to demonstrate the efficiency of the proposed method. The accuracy of the developed model can be further increased, which is subject to future research.",
keywords = "ARIMA, Forecasting, generation, solar, stationarity, time series",
author = "Sharif Atique and Subrina Noureen and Vishwajit Roy and Vinitha Subburaj and Stephen Bayne and Joshua MacFie",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 9th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2019 ; Conference date: 07-01-2019 Through 09-01-2019",
year = "2019",
month = mar,
day = "12",
doi = "10.1109/CCWC.2019.8666481",
language = "English",
series = "2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "114--119",
editor = "Satyajit Chakrabarti and Saha, {Himadri Nath}",
booktitle = "2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019",
}