Prognozowanie cen energii: Hybryda autoregresji i najbliższych sąsiadów (ARHNN)
[Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method]

Weronika Nitka (Doctoral School - Management, PWr, Wrocław)
Dimitrios Sotiros (Dept. of Operations Research & Business Intelligence, PWr, Wrocław)


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.

Based on: W.Nitka. T.Serafin, D.Sotiros (2021) Forecasting Electricity Prices: 
Autoregressive Hybrid Nearest Neighbors (ARHNN) method, ICCS 2021, LNCS, Springer