Forecasting Day-Ahead Electricity Prices: Utilizing Hourly Prices

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. This version -May 6, 2013 Terms of use: Documents in AbstractThe daily average price of electricity represents the price of electricity to be delivered over the full next day and serves as a key reference price in the electricity market. It is an aggregate that equals the average of hourly prices for delivery during each of the 24 individual hours. This paper demonstrates that the disaggregated hourly prices contain useful predictive information for the daily average price. Multivariate models for the full panel of hourly prices signicantly outperform univariate models of the daily average price, with reductions in Root Mean Squared Error of up to 16%. Substantial care is required in order to achieve these forecast improvements. Rich multivariate models are needed to exploit the relations between dierent hourly prices, but the risk of overtting must be mitigated by using dimension reduction techniques, shrinkage and forecast combinations.

Computing electricity spot price prediction intervals using quantile regression and forecast averaging

“…This fact is a good motivation for considering combining electricity spot price forecasts. Surprisingly, this approach has not been undertaken in the literature until very recently, see Bordignon et al (2013), Nowotarski et al (2014) and Raviv et al (2013). All three cited papers yield similar conclusions-they support the benefits of combining forecasts for deriving more accurate and more robust point forecasts of electricity spot prices.…”

“…Despite this popularity, combining forecasts has not been discussed widely in the context of electricity spot prices. Only very recently Bordignon et al (2013), Nowotarski et al (2014) and Raviv et al (2013) have provided empirical support for the benefits of combining forecasts to obtain better point predictions of electricity spot prices.…”

Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting

“…According to Definition 4, we have modified the constriction factor based on Equation (6). The introduction of constriction factors is beneficial in ensuring the convergence of the particles and cancelling the constraint of the border on the velocity.…”

“…On the one hand, the electricity price could balance the economic interests of participants in the market. On the other hand, the market would also be faced with large risks in electricity price owing to the fluctuation of the wind [6]. Based on the discussion above, we can see that forecasting the electrical power system with high accuracy and reliability is a widespread difficulty; however, it is of great significance.…”

Electricity Price Forecasting by Averaging Dynamic Factor Models

“…Furthermore, [20] obtain forecasts for the daily average price employing dimensionality reduction techniques, as well as the forecast combination of several models for hourly prices. Other references are [21], who use averaging to obtain wind speed, solar irradiation and temperature forecasts, which are employed to estimate prices; and [22], who forecast hourly electricity prices for the Spanish market by weighting seasonal ARIMA (with exogenous variables) and seasonal dynamic factor models of similar performance.…”