Research Article

Electricity Demand And Renewable Energy Analysis Using Opsd Data From Selected European Countries

Volume: 13 Number: 1 July 11, 2026
TR EN

Electricity Demand And Renewable Energy Analysis Using Opsd Data From Selected European Countries

Abstract

This study analyzes electricity demand and wind energy production data in detail using the Open Power System Data (OPSD) dataset obtained from European countries such as Germany, Spain, France, Italy, and Poland. The OPSD database is considered a reliable source for both academic research and energy market applications due to its provision of high-resolution time-series data. The study examines both short-term and long-term trends and analyzes the seasonal structure of electricity demand and the differences between weekdays and weekends. The results show that wind energy production exhibits strong periodic behavior, and significant autocorrelation peaks observed, particularly in the first 24–48 lag intervals, reflect short-term cyclical dependencies. Cross-country comparisons show that Germany and France exhibit relatively stable trend structures, while Spain and Italy display higher variability and more pronounced seasonal fluctuations. Poland, on the other hand, exhibits stronger irregular components, indicating increased system variability. These findings highlight the importance of considering both seasonal dynamics and short-term variability in energy system planning. The study provides a comprehensive framework for understanding the structural characteristics of electricity demand and renewable energy production, contributing to the development of more resilient and reliable energy systems.

Keywords

Time series analysis, renewable energy integration, electricity demand forecasting, energy systems planning

References

  1. Wiese F, et al. Open Power System Data – Frictionless data for electricity system modelling, Applied Energy. 2019; 236:401–409. https://doi.org/10.1016/j.apenergy.2018.11.097
  2. Box GEP, Jenkins GM, Reinsel GC, Ljung GM. Time series analysis : forecasting and control. Hoboken, New Jersey: John Wiley & Sons, 2015.
  3. Hyndman RJ, Athanasopoulos G. Forecasting: Principles and Practice, 2nd ed. Heathmont, Vic.: Otexts, 2018. Available: https://otexts.com/fpp2/
  4. Nti IK, Teimeh M, Nyarko-Boateng O, Adekoya AF, Electricity load forecasting: a systematic review, Journal of Electrical Systems and Information Technology. 2020; 7(1). https://doi.org/10.1186/s43067-020-00021-8.
  5. Ashtar D, Mohammadi Ziabari SS, Alsahag AM, Hybrid forecasting for sustainable electricity demand in the Netherlands using SARIMAX, SARIMAX-LSTM, and sequence-to-sequence deep learning models, Sustainability. 2025; 17(16).
  6. Franco A, and Pagliantini C. Forecasting Electricity Demand in Renewable-Integrated Systems: A Case Study from Italy Using Recurrent Neural Networks, Electricity.2025; 6(2):30–30. https://doi.org/10.3390/electricity6020030
  7. Telli A, Özkan A, Enerji Politikasi Üzerinden Türkiye’nin Sürdürülebilirlik Ve Kalkinma Hedeflerinin Değerlendirilmesi, KTÜSBD, 2024; 14(28): 332–352.
  8. IEA (2022), World Energy Outlook 2022, IEA, Paris https://www.iea.org/reports/world-energy-outlook-2022, Licence: CC BY 4.0 (report); CC BY NC SA 4.0 (Annex A)Klein, M., et al. (2019). Open Power System Data. Energy Reports, 5, 827-835.
  9. AKTAŞ C, Ulusal enerji tüketiminin değerlendirmesi ve istatistiksel tahmini, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 2019; 8(4): 1422–1431. https://doi.org/10.17798/bitlisfen.542963
  10. Ladjal B,et al. Hybrid deep learning CNN-LSTM model for forecasting direct normal irradiance: a study on solar potential in Ghardaia, Algeria, Scientific Reports. 2025; 15(1). https://doi.org/10.1038/s41598-025-94239-z
APA
Altun, S. (2026). Electricity Demand And Renewable Energy Analysis Using Opsd Data From Selected European Countries. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 13(1), 1-17. https://doi.org/10.54365/adyumbd.1862489
AMA
1.Altun S. Electricity Demand And Renewable Energy Analysis Using Opsd Data From Selected European Countries. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2026;13(1):1-17. doi:10.54365/adyumbd.1862489
Chicago
Altun, Sinan. 2026. “Electricity Demand And Renewable Energy Analysis Using Opsd Data From Selected European Countries”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 13 (1): 1-17. https://doi.org/10.54365/adyumbd.1862489.
EndNote
Altun S (July 1, 2026) Electricity Demand And Renewable Energy Analysis Using Opsd Data From Selected European Countries. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 13 1 1–17.
IEEE
[1]S. Altun, “Electricity Demand And Renewable Energy Analysis Using Opsd Data From Selected European Countries”, Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 13, no. 1, pp. 1–17, July 2026, doi: 10.54365/adyumbd.1862489.
ISNAD
Altun, Sinan. “Electricity Demand And Renewable Energy Analysis Using Opsd Data From Selected European Countries”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi 13/1 (July 1, 2026): 1-17. https://doi.org/10.54365/adyumbd.1862489.
JAMA
1.Altun S. Electricity Demand And Renewable Energy Analysis Using Opsd Data From Selected European Countries. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2026;13:1–17.
MLA
Altun, Sinan. “Electricity Demand And Renewable Energy Analysis Using Opsd Data From Selected European Countries”. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, vol. 13, no. 1, July 2026, pp. 1-17, doi:10.54365/adyumbd.1862489.
Vancouver
1.Sinan Altun. Electricity Demand And Renewable Energy Analysis Using Opsd Data From Selected European Countries. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi. 2026 Jul. 1;13(1):1-17. doi:10.54365/adyumbd.1862489