Algorithm

Residual

Engineering

Biology

Statistics

Time series

Energy (signal processing)

Computer science

Akaike information criterion

Solar energy

Paleontology

Physics

Database

Mathematics

Information Criteria

Econometrics

Autoregressive integrated moving average

Environmental science

Series (stratigraphy)

Meteorology

Model selection

Data modeling

Electrical engineering

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.

Forecasting of total daily solar energy generation using ARIMA: A case study