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Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices

Author

Listed:
  • Stefano Frizzo Stefenon

    (Digital Industry Center, Fondazione Bruno Kessler, 38123 Trento, Italy
    Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy)

  • Laio Oriel Seman

    (Graduate Program in Applied Computer Science, University of Vale do Itajai, Itajai 88302-901, Brazil)

  • Viviana Cocco Mariani

    (Department of Electrical Engineering, Federal University of Parana, Curitiba 81530-000, Brazil
    Mechanical Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba 80215-901, Brazil)

  • Leandro dos Santos Coelho

    (Department of Electrical Engineering, Federal University of Parana, Curitiba 81530-000, Brazil
    Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba 80215-901, Brazil)

Abstract

The cost of electricity and gas has a direct influence on the everyday routines of people who rely on these resources to keep their businesses running. However, the value of electricity is strongly related to spot market prices, and the arrival of winter and increased energy use owing to the demand for heating can lead to an increase in energy prices. Approaches to forecasting energy costs have been used in recent years; however, existing models are not yet robust enough due to competition, seasonal changes, and other variables. More effective modeling and forecasting approaches are required to assist investors in planning their bidding strategies and regulators in ensuring the security and stability of energy markets. In the literature, there is considerable interest in building better pricing modeling and forecasting frameworks to meet these difficulties. In this context, this work proposes combining seasonal and trend decomposition utilizing LOESS (locally estimated scatterplot smoothing) and Facebook Prophet methodologies to perform a more accurate and resilient time series analysis of Italian electricity spot prices. This can assist in enhancing projections and better understanding the variables driving the data, while also including additional information such as holidays and special events. The combination of approaches improves forecast accuracy while lowering the mean absolute percentage error (MAPE) performance metric by 18% compared to the baseline model.

Suggested Citation

  • Stefano Frizzo Stefenon & Laio Oriel Seman & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2023. "Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices," Energies, MDPI, vol. 16(3), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1371-:d:1049947
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    2. Zhigang Liu & Jin Wang & Tao Tao & Ziyun Zhang & Siyi Chen & Yang Yi & Shuang Han & Yongqian Liu, 2023. "Wave Power Prediction Based on Seasonal and Trend Decomposition Using Locally Weighted Scatterplot Smoothing and Dual-Channel Seq2Seq Model," Energies, MDPI, vol. 16(22), pages 1-17, November.
    3. 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).
    4. Aleksandr N. Grekov & Elena V. Vyshkvarkova & Aleksandr S. Mavrin, 2024. "Forecasting and Anomaly Detection in BEWS: Comparative Study of Theta, Croston, and Prophet Algorithms," Forecasting, MDPI, vol. 6(2), pages 1-14, May.
    5. Joohyun Jang & Woonyoung Jeong & Sangmin Kim & Byeongcheon Lee & Miyoung Lee & Jihoon Moon, 2023. "RAID: Robust and Interpretable Daily Peak Load Forecasting via Multiple Deep Neural Networks and Shapley Values," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    6. Zhewei Huang & Yawen Yi, 2024. "Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer," Sustainability, MDPI, vol. 16(17), pages 1-25, September.
    7. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
    8. Winita Sulandari & Yudho Yudhanto & Sri Subanti & Crisma Devika Setiawan & Riskhia Hapsari & Paulo Canas Rodrigues, 2023. "Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data," Energies, MDPI, vol. 16(22), pages 1-16, November.
    9. Rafael Ninno Muniz & Carlos Tavares da Costa Júnior & William Gouvêa Buratto & Ademir Nied & Gabriel Villarrubia González, 2023. "The Sustainability Concept: A Review Focusing on Energy," Sustainability, MDPI, vol. 15(19), pages 1-22, September.
    10. Firuz Kamalov & Hana Sulieman & Sherif Moussa & Jorge Avante Reyes & Murodbek Safaraliev, 2024. "Powering Electricity Forecasting with Transfer Learning," Energies, MDPI, vol. 17(3), pages 1-13, January.
    11. Saleh Albahli, 2025. "LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting," Energies, MDPI, vol. 18(2), pages 1-23, January.

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