Efficient modeling and forecasting of electricity spot prices

Abstract

The increasing importance of renewable energy, especially solar and wind power, has led to new forces in the formation of electricity prices. Hence, this paper introduces an econometric model for the hourly time series of electricity prices of the European Power Exchange (EPEX) which incorporates specific features like renewable energy. The model consists of several sophisticated and established approaches and can be regarded as a periodic VAR-TARCH with wind power, solar power, and load as influences on the time series. It is able to map the distinct and well-known features of electricity prices in Germany. An efficient iteratively reweighted lasso approach is used for the estimation. Moreover, it is shown that several existing models are outperformed by the procedure developed in this paper.

Automated Variable Selection and Shrinkage for Day-Ahead Electricity Price Forecasting

“…Like in [12,18,[25][26][27]30], the modeling is implemented separately across the hours, leading to 24 sets of parameters for each day the forecasting exercise is performed. As Ziel [18] notes, when we compare the forecasting performance of relatively simple models implemented separately across the hours and jointly for all hours (like in [9,[34][35][36]), the latter generally performs better for the first half of the day, whereas the former are better in the second half of the day. At the same time, models implemented separately across the hours offer more flexibility by allowing for time-varying cross-hour dependency in a straightforward manner.…”

“…As has been noted in a number of studies, be it statistical or computational intelligence, a key point in EPF is the appropriate choice of explanatory variables [1,[4][5][6][7][8][9][10][11]. The typical approach has been to select predictors in an ad hoc fashion, sometimes using expert knowledge, seldom based on some formal validation procedures.…”

“…In a recent neural network study, Keles et al [11] combined the k-nearest-neighbor algorithm with backward elimination to select the most appropriate input variables out of more than 50 fundamental parameters or lagged versions of these parameters. Finally, Ziel et al [9,18] used the lasso to sparsify very large sets of model parameters (well over 100). They used time-varying coefficients to capture the intra-day dependency structure, either using B-splines and one large regression model for all hours of the day [9] or, more efficiently, using a set of 24 regression models for the 24 h of the day [18].…”

“…Finally, Ziel et al [9,18] used the lasso to sparsify very large sets of model parameters (well over 100). They used time-varying coefficients to capture the intra-day dependency structure, either using B-splines and one large regression model for all hours of the day [9] or, more efficiently, using a set of 24 regression models for the 24 h of the day [18].…”

Electricity price forecasting using asymmetric fuzzy neural network systems

“…Hence, electricity price can be considered as more volatile than the electricity load. It is worth mentioned, that during the introduction of dynamic pricing strategies, electricity prices become even more volatile, where the daily average price were changed by up to 50% while other commodities exhibited about 5% change [2].…”