Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method

Weronika Nitka, Tomasz Serafin and Dimitrios Sotiros

No WORMS/21/06, WORking papers in Management Science (WORMS) from Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology

Abstract: The ongoing reshape of electricity markets has significantly stimulated electricity trading. Limitations in storing electricity as well as on-the-fly changes in demand and supply dynamics, have led price forecasts to be a fundamental aspect of traders' economic stability and growth. In this perspective, there is a broad literature that focuses on developing methods and techniques to forecast electricity prices. In this paper, we develop a new hybrid method, called ARHNN, for electricity price forecasting (EPF) in day-ahead markets. A well performing autoregressive model, with exogenous variables, is the main forecasting instrument in our method. Contrarily to the traditional statistical approaches, in which the calibration sample consists of the most recent and successive observations, we employ the k-nearest neighbors (k-NN) instance-based learning algorithm and we select the calibration sample based on a similarity (distance) measure over a subset of the autoregressive model's variables. The optimal levels of the k-NN parameter are identified during the validation period in a way that the forecasting error is minimized. We apply our method in the EPEX SPOT market in Germany. The results reveal a significant improvement in accuracy compared to commonly used approaches.

Keywords: Electricity price forecasting; Day-ahead market; ARX; k-nearest neighbors (search for similar items in EconPapers)
JEL-codes: C22 C32 C51 C53 Q41 Q47 (search for similar items in EconPapers)
Pages: 15 pages
Date: 2021-04-10
New Economics Papers: this item is included in nep-ene and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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https://worms.pwr.edu.pl/RePEc/ahh/wpaper/WORMS_21_06.pdf Original version, 2021 (application/pdf)

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