Wholesale electricity price forecasting by Quantile Regression and Kalman Filter method

Mohammad Reza Monjazeb, Hossein Amiri and Akram Movahedi

Energy, 2024, vol. 290, issue C

Abstract: One of the most significant issues in all nations is the price of electricity. Quantile Regression and the Kalman Filter method are used in this study to forecast the price of electricity in the pay-as-bid market, which is used in many nations, including Iran, Italy, Germany, the UK and many more. The results of our study suggest that Quantile Regression is the most accurate model, particularly when 20th compared to 90th quantiles and the Kalman Filter method. Moreover, geographical and climate considerations play a significant role in setting electricity pricing. Also, it is important to consider the positive correlations between the electricity price and temperature, as well as its negative and uncertain relationship with other variables, including wind speed and humidity.

Keywords: Forecasting; Electricity price; Quantile regression; Kalman Filter; Pay-as-bid (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:290:y:2024:i:c:s0360544223033194

DOI: 10.1016/j.energy.2023.129925

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