Seasonality-Aware Time Series Modeling for Monthly Solar PV Power Forecasting: A Comparative Study

Ecem Nur Sener; Ebru Idman; Osman Yildirim

Seasonality-Aware Time Series Modeling for Monthly Solar PV Power Forecasting: A Comparative Study

Author(s)	Ms. Ecem Nur Sener, Dr. Ebru Idman, Prof. Dr. Osman Yildirim
Country	Turkey
Abstract	Accurate monthly forecasting of solar photovoltaic (PV) energy production is essential for medium-term energy planning, grid integration, and operational decision-making. Although monthly photovoltaic (PV) forecasting is still difficult to perform because it has large seasonal variability and there are few sources of high resolution weather data, this paper presents an objective comparison of three models of time series forecasting; Double Exponential Smoothing (DES); Holt-Winters Triple Exponential Smoothing (TES); and Seasonal Autoregressive Integrated Moving Average (SARIMA). Each model was tested on operational data from a grid connected solar PV power station over the period 2022 – 2025 and demonstrated a very distinct annual seasonality pattern. Using standard error measurements such as root mean square error (RMSE), mean absolute error (MAE), and bias each model’s ability to forecast the power output accurately was evaluated. The results indicated that although DES did not take into consideration any seasonal patterns, it had large systematic errors and therefore poor forecasting performance. TES improved upon the forecasting accuracy of DES by incorporating multiplicative seasonal patterns, however, TES also failed to capture rapid changes in power output at times when the PV power plant was producing at its maximum capacity. Conversely, SARIMA provided the best forecasting accuracy and the least amount of bias, thus allowing for accurate modeling of trends and annual seasonal cycles. These results demonstrate the need for explicit inclusion of seasonal patterns in monthly PV forecasting, and confirm SARIMA as being a robust and reliable method for use with seasonal uncertainty.
Keywords	Solar Photovoltaic Power Forecasting, Monthly Energy Prediction, Seasonal Time Series Analysis, SARIMA, Holt-Winters Exponential Smoothing, Double Exponential Smoothing, Seasonal Uncertainty
Field	Engineering
Published In	Volume 7, Issue 1, January-February 2026
Published On	2026-02-03

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Seasonality-Aware Time Series Modeling for Monthly Solar PV Power Forecasting: A Comparative Study

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