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Analysis of the Effectiveness of ARIMA, SARIMA, and SVR Models in Time Series Forecasting: A Case Study of Wind Farm Energy Production

Author

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  • Kamil Szostek

    (Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland)

  • Damian Mazur

    (Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland)

  • Grzegorz Drałus

    (Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, 35-959 Rzeszów, Poland)

  • Jacek Kusznier

    (Faculty of Electrical Engineering, Bialystok University of Technology, 15-351 Białystok, Poland)

Abstract

The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). This study utilizes data from two wind farms located in Poland—‘Gizałki’ and ‘Łęki Dukielskie’—to exclude the possibility of biased results due to specific characteristics of a single farm and to allow for a more comprehensive comparison of the effectiveness of both time series analysis methods. Model parameterization was optimized through a grid search based on the Mean Absolute Percentage Error ( MAPE ). The performance of the best models was evaluated using Mean Bias Error ( MBE ), MAPE , Mean Absolute Error ( MAE ), and R2Score . For the Gizałki farm, the ARIMA model outperformed SARIMA and SVR, while for the Łęki Dukielskie farm, SARIMA proved to be the most accurate, highlighting the importance of optimizing seasonal parameters. The SVR method demonstrated the lowest effectiveness for both datasets. The results indicate that the ARIMA and SARIMA models are effective for forecasting wind farm energy production. However, their performance is influenced by the specificity of the data and seasonal patterns. The study provides an in-depth analysis of the results and offers suggestions for future research, such as extending the data to include multidimensional time series. Our findings have practical implications for enhancing the accuracy of wind farm energy forecasts, which can significantly improve operational efficiency and planning.

Suggested Citation

  • Kamil Szostek & Damian Mazur & Grzegorz Drałus & Jacek Kusznier, 2024. "Analysis of the Effectiveness of ARIMA, SARIMA, and SVR Models in Time Series Forecasting: A Case Study of Wind Farm Energy Production," Energies, MDPI, vol. 17(19), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4803-:d:1485694
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    References listed on IDEAS

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    1. Ziquan Zhao & Jing Bai, 2024. "Ultra-Short-Term Wind Power Forecasting Based on the MSADBO-LSTM Model," Energies, MDPI, vol. 17(22), pages 1-17, November.
    2. Wangjian Li & Yiwen Zhang & Yaoyao Liu, 2025. "Multivariate Air Quality Forecasting with Residual Nested LSTM Neural Network Based on DSWT," Sustainability, MDPI, vol. 17(5), pages 1-23, March.

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