Short- to Mid-term Day-Ahead Electricity Price Forecasting Using Futures

Abstract

Due to the liberalization of markets, the change in the energy mix and the surrounding energy laws, electricity research is a dynamically altering field with steadily changing challenges. One challenge especially for investment decisions is to provide reliable short to mid-term forecasts despite high variation in the time series of electricity prices. This paper tackles this issue in a promising and novel approach. By combining the precision of econometric autoregressive models in the short-run with the expectations of market participants reflected in future prices for the short-and mid-run we show that the forecasting performance can be vastly increased while maintaining hourly precision. We investigate the day-ahead electricity price of the EPEX Spot for Germany and Austria and setup a model which incorporates the Phelix future of the EEX for Germany and Austria. The model can be considered as an AR24-X model with one distinct model for each hour of the day. We are able to show that future data contains relevant price information for future time periods of the day-ahead electricity price. We show that relying only on deterministic external regressors can provide stability for forecast horizons of multiple weeks. By implementing a fast and efficient lasso estimation approach we demonstrate that our model can outperform several other models in the literature.

Predictive Trading Strategy for Physical Electricity Futures

“…On the other hand, several studies on European markets have shown the inefficiency of such markets, revealing that the futures prices are not unbiased predictors of the future spot prices [5][6][7]. Nevertheless, other studies, such as [8], have successfully used the futures price to predict the electricity price on the spot market. The differences in the predictive capacity of futures prices among diverse markets may be due to the different types of electricity supply in each market.…”

“…In the training process of the single hidden layer feedforward neural network with the ELM algorithm, the bias of each neuron (node) in the hidden layer and the weights connecting input and hidden layers are randomly chosen (i.e., the a i and b i values). From these random values, the matrix H is determined according the input samples, and the training process is converted into solving, by least squares, the linear equations Hβ = O, to obtain β, what can be represented by (8):…”

Using Machine Learning to Profit on the Risk Premium of the Nordic Electricity Futures

“…This is indirectly documented in these papers, as lagged financial variables are also included in the regression models, with positive results. In fact, some papers highlight the predictive power of futures prices (e.g., Huisman and Kilic, 2012;Paraschiv et al, 2015;Aoude et al, 2016;Ferreira and Sebastião, 2018;Steinert and Ziel, 2019).…”

Electricity price forecasting

“…However, as show, an even more inuential explanatory variable may be P d 1;24 , i.e., the last known price, or more generally yesterday's late evening prices. Other`less intuitive' regressors include yesterday's prices for the neighboring hours (e.g., P d 1;18 when predicting P d;17 ) and very recent prices of daily futures contracts with delivery on day d, and what may seem even more surprising with delivery on day d + 1, but only when predicting the evening hours 20-24 (Steinert & Ziel, 2019). A likely explanation for the latter is that the evening hours of day d are close to the early morning hours of day d + 1, when the delivery of these futures contracts begin.…”