# Predictive Modeling of Photovoltaic Energy Yield Using an ARIMA Approach
## 5 Conclusions  
This study presented a robust method for forecasting the future performance of a grid-connected rooftop photovoltaic system by applying ARIMA models to both the specific PV system yield and the total yield. Through careful model selection and calibration, our approach demonstrated superior predictive accuracy compared to standard software-generated models, offering more reliable forecasts. This enhanced forecasting ability is crucial for effective energy management, grid integration, and optimizing the use of renewable energy resources. The main advantages of the proposed approach lie in its flexibility and accuracy. Via ARIMA modeling, we can make reliable forecasts for numerous periods in the future, while other popular methods' forecasts worsen their quality with the number of predictions performed. What is more, the method is robust and light, requiring less historical data and fewer resources to calibrate than a neural network, for example. Regarding the precision, it is demonstrated by the magnitude of the standard errors, the number of errors lying outside their confidence intervals, and the values of the information criteria. By leveraging \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Month**} & **Forecast** & **Conf. Int.** & **Forecast** & **Conf. Int.** \\  & **ARIMA (2,0,1)** & **ARIMA (2,0,1)** & **ARIMA (3,0,5)** & **ARIMA (3,0,5)** \\ \hline August 2023 & 887.41 & [682.85; 1091.97] & 838.04 & [666.42; 1009.67] \\ September 2023 & 769.69 & [487.50; 1051.88] & 690.56 & [502.76; 878.36] \\ October 2023 & 616.33 & [302.73; 929.93] & 513.46 & [321.10; 705.82] \\ November 2023 & 469.59 & [151.80; 787.37] & 377.03 & [184.68; 569.37] \\ December 2023 & 365.36 & [45.39; 685.33] & 291.46 & [99.13; 483.80] \\ January 2024 & 324.98 & [\(-\)12.39; 662.35] & 283.33 & [91.00; 475.67] \\ February 2024 & 351.45 & [\(-\)14.64; 717.55] & 351.02 & [158.66; 543.38] \\ March 2024 & 430.77 & [39.55; 821.99] & 480.27 & [287.88; 672.66] \\ April 2024 & 537.27 & [133.53; 941.02] & 632.93 & [440.53; 825.32] \\ May 2024 & 641.36 & [235.55; 1047.17] & 772.08 & [579.70; 964.47] \\ June 2024 & 717.30 & [310.91; 1123.68] & 856.98 & [646.62; 1049.35] \\ July 2024 & 749.25 & [336.55; 1161.94] & 868.72 & [676.35; 1061.09] \\ August 2024 & 734.10 & [309.78; 1158.41] & 800.49 & [608.10; 992.88] \\ September 2024 & 680.85 & [245.42; 1116.27] & 674.13 & [481.70; 866.55] \\ October 2024 & 607.01 & [165.58; 1048.44] & 519.61 & [327.18; 712.04] \\ November 2024 & 533.31 & [90.72; 975.90] & 381.73 & [189.31; 574.14] \\ December 2024 & 478.13 & [35.38; 920.88] & 293.54 & [101.14; 485.94] \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Forecasts and confidence intervals with 95% confidence level. Series: total yield. the ARIMA methodology, we can account for the complex temporal dynamics of solar energy production, resulting in more precise predictions. This, in turn, allows for better planning and decision-making for households, utility providers, and energy policymakers. Additionally, while the model calibration requires initial expertise, our method streamlines this process through an algorithm that refines software-suggested models, making it more accessible for practitioners. Once calibrated, the models can be used consistently with minimal adjustments, providing a practical and cost-effective forecasting tool. We hope the proposed ARIMA-based forecasting approach contributes to the field of renewable energy, facilitating the efficient integration of solar power into the energy grid and promoting the broader adoption of photovoltaic systems. A possible way to continue this research is to use exogenous inputs such as average monthly temperature and solar irradiance or to use the vector ARIMA to forecast both yields simultaneously. Investigating other approaches such as neural networks or systems of modified ordinary differential equations is always an option.