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Forecasting Electricity Prices for the Feasibility of Renewable Energy Plants

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Advances in Intelligent Manufacturing and Service System Informatics (IMSS 2023)

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

Feasibility studies in assessing the viability of renewable energy investments involve conducting economic analyses to evaluate crucial performance criteria such as return on investment and payback period. In these analyses, it is essential to calculate the profitability derived from the electricity generated and sold, considering the investment and operational costs. At this point, electricity prices emerge as a determining parameter. However, accurately predicting electricity prices is challenging due to the need to account for production costs, as well as regulations and policies within the framework of the free market mechanism. Moreover, compared to other traded commodities, electricity prices are further complicated by electricity’s inability to store, the necessity of instantaneous balancing of production and consumption, and the high seasonality of demand for domestic, industrial and commercial electricity. On the other hand, precise forecasting of future electricity prices is a critical factor that enhances the accuracy of economic analyses. This study employs the Prophet Algorithm, which utilizes time series analysis and considers historical electricity prices to make periodic predictions of future electricity prices. The Prophet algorithm is specifically designed to capture the evidence of deterministic trend and seasonality, and at the same time the effects of an econometric shock, providing reliable results in forecasting electricity prices. Our study has examined and compared the forecasting performance of Python’s Prophet Algorithm and Excel Estimator. Although the Excel Estimator did not achieve the same level of precision, it produced results within an acceptable range.

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Author information

Authors and Affiliations

  1. Turkiye İş Bank Inc., Adapazari, Sakarya, Turkey

    Bucan Türkmen

  2. Sakarya University Engineering Faculty/Department of Industrial Engineering, Serdivan, Sakarya, Turkey

    Sena Kır

  3. Sakarya University of Applied Sciences Faculty of Applied Sciences, Department of International Trade and Business, Serdivan, Sakarya, Turkey

    Nermin Ceren Türkmen

Authors
  1. Bucan Türkmen
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  2. Sena Kır
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  3. Nermin Ceren Türkmen
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Corresponding author

Correspondence to Sena Kır .

Editor information

Editors and Affiliations

  1. Istanbul Medipol University, Istanbul, Türkiye

    Zekâi Şen

  2. Department of Industrial Engineering, Sakarya University, Serdivan, Sakarya, Türkiye

    Özer Uygun

  3. Faculty of Applied Sciences, Sakarya University of Applied Sciences, Kaynarca, Sakarya, Türkiye

    Caner Erden

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Türkmen, B., Kır, S., Türkmen, N.C. (2024). Forecasting Electricity Prices for the Feasibility of Renewable Energy Plants. In: Şen, Z., Uygun, Ö., Erden, C. (eds) Advances in Intelligent Manufacturing and Service System Informatics. IMSS 2023. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-99-6062-0_75

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  • DOI: https://doi.org/10.1007/978-981-99-6062-0_75

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-6061-3

  • Online ISBN: 978-981-99-6062-0

  • eBook Packages: EngineeringEngineering (R0)

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