Economic Implications of Enhanced Forecast Accuracy: The Case of Photovoltaic Feed-In Forecasts

FCN Working Paper No. 6/2015

38 Pages Posted: 21 Oct 2015 Last revised: 22 Jul 2017

See all articles by Oliver Ruhnau

Oliver Ruhnau

RWTH Aachen University

Patrick Hennig

Grundgrün Energie GmbH

Reinhard Madlener

RWTH Aachen University

Date Written: July 1, 2017

Abstract

Forecasts are usually evaluated in terms of accuracy. With regard to application, the question arises if the most accurate forecast is also optimal in terms of forecast related costs and risks. Combining insights from research and practice, we show that this is indeed not necessarily the case. Our analysis is grounded in the dynamic field of short-term forecasting of solar electricity feed-in. A clear sky model is implemented and combined with a linear model, an autoregressive model, and an artificial neural network. These models are applied to a portfolio of ten large-scale photovoltaic systems in Germany. We compare the different models in order to quantify the connection between errors and costs. We find that apart from accuracy, correlation with market prices is an important characteristic of forecasts when economic implications are considered as important.

Keywords: Forecasting evaluation, renewable energy, electricity markets, balancing costs, artificial neural networks, clear sky model, Germany

Suggested Citation

Ruhnau, Oliver and Hennig, Patrick and Madlener, Reinhard, Economic Implications of Enhanced Forecast Accuracy: The Case of Photovoltaic Feed-In Forecasts (July 1, 2017). FCN Working Paper No. 6/2015. Available at SSRN: https://ssrn.com/abstract=2676629 or http://dx.doi.org/10.2139/ssrn.2676629

Oliver Ruhnau (Contact Author)

RWTH Aachen University ( email )

Templergraben 55
52056 Aachen, 52056
Germany

Patrick Hennig

Grundgrün Energie GmbH ( email )

Uhlandstraße 181/183
Berlin, 10623
Germany

Reinhard Madlener

RWTH Aachen University ( email )

School of Business and Economics / E.ON ERC
Mathieustraße 10
Aachen, 52074
Germany
+49 241 80 49 820 (Phone)
+49 241 80 49 829 (Fax)

HOME PAGE: http://www.eonerc.rwth-aachen.de/fcn

Register to save articles to
your library

Register

Paper statistics

Downloads
43
Abstract Views
553
PlumX Metrics