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Advancing short-term load forecasting with decomposed Fourier ARIMA: A case study on the Greek energy market

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  • Karamolegkos, Spyridon
  • Koulouriotis, Dimitrios E.

Abstract

Accurate short-term load forecasting (STLF) is crucial for the operational stability and efficiency of modern energy systems, particularly in markets experiencing increasing complexity. Traditional statistical methods, despite their robustness, often struggle to model multifrequency seasonality. This paper introduces a novel forecasting model, the decomposed Fourier ARIMA (FARIMA), designed to address multifrequency seasonal patterns in time series data with a specific application in this study to electricity consumption data. The FARIMA model combines Fourier decomposition to isolate periodic components, polynomial regression to capture trends, and ARIMA to model the residuals. The study evaluates optimal training periods and benchmarks FARIMA's performance against mature traditional methods, specifically SARIMA and Holt-Winters, which are widely used in short-term load forecasting. Using Greek energy market data, FARIMA consistently outperformed SARIMA and Holt-Winters across different setups. For a one-year training period, it achieved a MAPE that was 1.59 percentage points lower than SARIMA's and 1.44 percentage points lower than Holt-Winters'. For a six-month training period, FARIMA achieved a MAPE that was 0.17 percentage points lower than SARIMA's and 3.33 percentage points lower than Holt-Winters'. Additionally, FARIMA demonstrated significant computational efficiency, achieving a runtime reduction of 98.5 % compared to SARIMA in both setups, due to its ability to simplify residual signals and use less complex ARIMA parameters. Results demonstrate FARIMA's superior forecasting accuracy, specifically for one-week-ahead predictions, with lower Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) compared to conventional models. The FARIMA model bridges gaps in traditional forecasting by effectively capturing multifrequency patterns. While its practical implications primarily focus on short-term load forecasting, for which it was specifically developed, FARIMA is fundamentally a new time series model. As such, it holds potential for application across a wide range of domains involving multifrequency time series data.

Suggested Citation

  • Karamolegkos, Spyridon & Koulouriotis, Dimitrios E., 2025. "Advancing short-term load forecasting with decomposed Fourier ARIMA: A case study on the Greek energy market," Energy, Elsevier, vol. 325(C).
    • Handle: RePEc:eee:energy:v:325:y:2025:i:c:s0360544225014963
    DOI: 10.1016/j.energy.2025.135854
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    as
    1. Deng, Song & Dong, Xia & Tao, Li & Wang, Junjie & He, Yi & Yue, Dong, 2024. "Multi-type load forecasting model based on random forest and density clustering with the influence of noise and load patterns," Energy, Elsevier, vol. 307(C).
    2. Wang, Jianguo & Han, Lincheng & Zhang, Xiuyu & Wang, Yingzhou & Zhang, Shude, 2023. "Electrical load forecasting based on variable T-distribution and dual attention mechanism," Energy, Elsevier, vol. 283(C).
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Wang, Zhijin & Liu, Xiufeng & Huang, Yaohui & Zhang, Peisong & Fu, Yonggang, 2023. "A multivariate time series graph neural network for district heat load forecasting," Energy, Elsevier, vol. 278(PA).
    5. Sholeh Hadi Pramono & Mahdin Rohmatillah & Eka Maulana & Rini Nur Hasanah & Fakhriy Hario, 2019. "Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System," Energies, MDPI, vol. 12(17), pages 1-16, August.
    6. Tan, Quanwei & Cao, Chunhua & Xue, Guijun & Xie, Wenju, 2024. "Short-term heating load forecasting model based on SVMD and improved informer," Energy, Elsevier, vol. 312(C).
    7. Wang, Chuang & Zhao, Haishen & Liu, Yang & Fan, Guojin, 2024. "Minute-level ultra-short-term power load forecasting based on time series data features," Applied Energy, Elsevier, vol. 372(C).
    8. Atif Maqbool Khan & Artur Wyrwa, 2024. "A Survey of Quantitative Techniques in Electricity Consumption—A Global Perspective," Energies, MDPI, vol. 17(19), pages 1-38, September.
    9. Peng, Daogang & Liu, Yu & Wang, Danhao & Zhao, Huirong & Qu, Bogang, 2024. "Multi-energy load forecasting for integrated energy system based on sequence decomposition fusion and factors correlation analysis," Energy, Elsevier, vol. 308(C).
    10. Sadaei, Hossein Javedani & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha & Lee, Muhammad Hisyam, 2019. "Short-term load forecasting by using a combined method of convolutional neural networks and fuzzy time series," Energy, Elsevier, vol. 175(C), pages 365-377.
    11. Khan, Taimoor & Choi, Chang, 2025. "Attention enhanced dual stream network with advanced feature selection for power forecasting," Applied Energy, Elsevier, vol. 377(PC).
    12. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    13. Chen, Yunxiao & Lin, Chaojing & Zhang, Yilan & Liu, Jinfu & Yu, Daren, 2024. "Day-ahead load forecast based on Conv2D-GRU_SC aimed to adapt to steep changes in load," Energy, Elsevier, vol. 302(C).
    14. de Azevedo Takara, Lucas & Teixeira, Ana Clara & Yazdanpanah, Hamed & Mariani, Viviana Cocco & dos Santos Coelho, Leandro, 2024. "Optimizing multi-step wind power forecasting: Integrating advanced deep neural networks with stacking-based probabilistic learning," Applied Energy, Elsevier, vol. 369(C).
    15. Chen, Haoyu & Huang, Hai & Zheng, Yong & Yang, Bing, 2024. "A load forecasting approach for integrated energy systems based on aggregation hybrid modal decomposition and combined model," Applied Energy, Elsevier, vol. 375(C).
    16. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).
    17. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos & Artemios-Anargyros Semenoglou & Gary Mulder & Konstantinos Nikolopoulos, 2023. "Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 74(3), pages 840-859, March.
    18. Wang, Xinlin & Wang, Hao & Li, Shengping & Jin, Haizhen, 2024. "A reinforcement learning-based online learning strategy for real-time short-term load forecasting," Energy, Elsevier, vol. 305(C).
    19. Xie, Xiangmin & Ding, Yuhao & Sun, Yuanyuan & Zhang, Zhisheng & Fan, Jianhua, 2024. "A novel time-series probabilistic forecasting method for multi-energy loads," Energy, Elsevier, vol. 306(C).
    20. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    21. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    22. Morais, Lucas Barros Scianni & Aquila, Giancarlo & de Faria, Victor Augusto Durães & Lima, Luana Medeiros Marangon & Lima, José Wanderley Marangon & de Queiroz, Anderson Rodrigo, 2023. "Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system," Applied Energy, Elsevier, vol. 348(C).
    23. Wan, Anping & Chang, Qing & AL-Bukhaiti, Khalil & He, Jiabo, 2023. "Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism," Energy, Elsevier, vol. 282(C).
    24. Pinheiro, Marco G. & Madeira, Sara C. & Francisco, Alexandre P., 2023. "Short-term electricity load forecasting—A systematic approach from system level to secondary substations," Applied Energy, Elsevier, vol. 332(C).
    25. Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Ribeiro, Gabriel Trierweiler & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2023. "Cooperative ensemble learning model improves electric short-term load forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    26. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    27. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
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