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ORIGINAL RESEARCH article
Front. Energy Res.
Sec. Energy Efficiency
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1477248
This article is part of the Research Topic Energy Management, Energy Efficiency Policies, and Energy System Studies View all 7 articles
Forecasting Day-Ahead Electric Power Prices with Functional Data Analysis
Provisionally accepted- 1 Department of Mathematics and Statistics, Bacha Khan University, Charsadda, Charsadda, Pakistan
- 2 Quaid-i-Azam University, Islamabad, Pakistan
- 3 Shaheed Benazir Bhutto Women University, Peshawar, Khyber Pakhtunkhwa, Pakistan
- 4 Department of Computer science, Electrical engineering and Mathematical Sciences, Faculty of Engineering and Science, Western Norway University of Applied Sciences, Sogndal, Sogn og Fjordane, Norway
- 5 Department of Statistics, Faculty of Science, University of Tabuk, Tabuk, Saudi Arabia
In today's competitive electric power markets, day-ahead electricity prices have complex features such as high frequency, high volatility, non-linearity, non-stationarity, mean reversion, multiple periodicities, and calendar effects. These complicated features make price forecasting difficult.To address this, the research examines the application of functional data analysis to forecasting day-ahead electric power prices. Compared to classical time series forecasting approaches, functional data analysis is more appealing since it anticipates the daily profile, allowing for short-term projections. This technique uses a functional autoregressive (FAR) and a functional autoregressive with exogenous predictors (FARX) model to predict the next-day electric power prices. In addition, standard time series forecasting models, including the autoregressive (AR), the ARX, the autoregressive integrated moving average (ARIMA), and the ARIMAX are also utilized for comparison. The model's prediction performance was evaluated using data on electricity prices from the British electricity market, considering forecast error indicators and the same forecast statistical test. The results show that the proposed functional models (FAR and FARX) outperform standard time series models. In comparison to the benchmark models (AR, ARX, ARIMA, ARIMAX, and the proposed FAR model), the FARX model reduces the day-ahead forecasting
Keywords: Electric power market, functional data analysis, Day-head electricity price forecasting, Classical time series models, Functional time series models
Received: 07 Aug 2024; Accepted: 25 Feb 2025.
Copyright: © 2025 Jan, Iftikhar, Tahir, Khan and Albalawi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hasnain Iftikhar, Quaid-i-Azam University, Islamabad, Pakistan
Mehak Khan, Department of Computer science, Electrical engineering and Mathematical Sciences, Faculty of Engineering and Science, Western Norway University of Applied Sciences, Sogndal, 6856, Sogn og Fjordane, Norway
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