Probabilistic mid- and long-term electricity price forecasting
Abstract
The liberalization of electricity markets and the development of renewable energy sources has led to new challenges for decision makers. These challenges are accompanied by an increasing uncertainty about future electricity price movements. The increasing amount of papers, which aim to model and predict electricity prices for a short period of time provided new opportunities for market participants. However, the electricity price literature seem to be very scarce on the issue of medium- to long-term price forecasting, which is mandatory for investment and political decisions. Our paper closes this gap by introducing a new approach to simulate electricity prices with hourly resolution for several months up to three years. Considering the uncertainty of future events we are able to provide probabilistic forecasts which are able to detect probabilities for price spikes even in the long-run. As market we decided to use the EPEX day-ahead electricity market for Germany and Austria. Our model extends the X-Model which mainly utilizes the sale and purchase curve for electricity day-ahead auctions. By applying our procedure we are able to give probabilities for the due to the EEG practical relevant event of six consecutive hours of negative prices. We find that using the supply and demand curve based model in the long-run yields realistic patterns for the time series of electricity prices and leads to promising results considering common error measures.
- Publication:
-
Renewable and Sustainable Energy Reviews
- Pub Date:
- October 2018
- DOI:
- arXiv:
- arXiv:1703.10806
- Bibcode:
- 2018RSERv..94..251Z
- Keywords:
-
- ACE;
- average coverage error;
- AIC;
- Akaike information criterion;
- AMAPE;
- adapted MAPE;
- ANEM;
- Australian National Electricity Market;
- ANN;
- artificial NN;
- ANOVA;
- analysis of variance;
- AWPI;
- average width of PIs;
- ARMAX;
- autoregressive moving average model with exogenous inputs;
- ARX;
- autoregressive model with exogenous inputs;
- BNetzA;
- German Federal Network Agency;
- BS;
- Brier Score;
- CRPS;
- continuous ranked probability score;
- CT;
- Christoffersen test;
- CWC;
- coverage width-based criterion;
- DC;
- direct current;
- DWD;
- German Meteorological Office;
- ECP;
- empirical coverage probability;
- ECR;
- evaluation criterion of resolution;
- EEG;
- German Renewable Energy Sources Act;
- ELM;
- extreme learning machine;
- ENTSO-E;
- European Network of Transmission System Operators;
- EPEX;
- European Power Exchange;
- EUPHEMIA;
- Pan-European Hybrid Electricity Market Integration Algorithm;
- GAMLSS;
- generalized additive models for location;
- scale and shape;
- GARCH;
- generalized autoregressive conditional heteroskedasticity;
- GDP;
- gross domestic product;
- GME;
- Gestore dei Mercati Energetici;
- k-NN;
- k nearest neighbor;
- Lasso;
- least absolute shrinkage and selection operator;
- LS;
- logarithmic scores;
- LSSVM;
- least-squares SVM;
- MAE;
- mean absolute error;
- MAPE;
- mean absolute percentage error;
- MLP;
- multi-layer perceptron;
- MPIW;
- mean prediction interval width;
- MSE;
- mean squared error;
- MSPE;
- mean squared percentage error;
- NN;
- neural network;
- NLPD;
- average negative log predictive density;
- NMPIW;
- normalised MPIW;
- OLS;
- ordinary least squares;
- OMIE;
- OMI-Polo Español;
- PBS;
- pinball score/loss;
- PCA;
- principal component analysis;
- PCR;
- Price Coupling of Regions;
- PI;
- prediction interval;
- PICP;
- PI coverage probability;
- PINAW;
- PI normalised average width;
- PITS;
- probability integral transform scores;
- PJM;
- Pennsylvania-New Jersey-Maryland Interconnection;
- RBF;
- radial basis function;
- RE;
- reliability evaluation criterion;
- RMSE;
- root MSE;
- SARIMAX;
- seasonal autoregressive integrated moving average with exogenous inputs;
- SE;
- sharpness criterion/score;
- SVM;
- support vector machine;
- UK;
- United Kingdom;
- US;
- United States;
- VEC;
- vector error correction model;
- WNN;
- weighted nearest neighbor;
- WS;
- Winkler score;
- Electricity prices;
- Probabilistic forecasting;
- Supply and demand;
- Long-term;
- Negative prices;
- Renewable energy;
- Statistics - Applications;
- Quantitative Finance - Statistical Finance;
- 62P05;
- 62P20;
- 62P12;
- 91G70;
- 62J07;
- I.5.1;
- J.4;
- J.2;
- J.1;
- I.2.6;
- I.6.3
- E-Print:
- accepted for: Renewable &