Daily Energy Price Forecasting Using a Polynomial NARMAX Model

Abstract

Energy prices are not easy to forecast due to nonlinearity from seasonal trends. In this paper a Nonlinear AutoRegressive Moving Average model with eXogenous input (NARMAX model) is created using nonlinear energy price data. To investigate if a short-term forecasting model is capable of predicting energy prices a model was developed using daily data from 2017 over a period of five weeks: observing 1 input lag prediction up to 12 input lag prediction for low-order polynomials (linear, quadratic, and cubic). Various input factors were explored (energy demand and previous price) with different combinations to observe which factors, if any, had an impact on the current price prediction. The results show that the generated NARMAX model is good at describing the inputoutput relationship of energy prices. The model works best with a low-order input regression parameter and linear polynomial degree. It was also noted that including energy demand as an input factor slightly improves the model validation results suggesting that there is a relationship between demand and energy prices.

Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling

“…The NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous input) model is the benchmark (trained with the optimal structure) for our case study. The statistical model is widely used in energy price forecasting to handle multiple nonlinear inputs [32,59]. The equation is represented as:…”