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

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  • Ruhnau, Oliver

    (RWTH Aachen University)

  • Hennig, Patrick

    (Grundgrün Energie GmbH)

  • Madlener, Reinhard

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

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.

Suggested Citation

  • Ruhnau, Oliver & Hennig, Patrick & Madlener, Reinhard, 2015. "Economic Implications of Enhanced Forecast Accuracy: The Case of Photovoltaic Feed-In Forecasts," FCN Working Papers 6/2015, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    • Handle: RePEc:ris:fcnwpa:2015_006
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    References listed on IDEAS

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    3. Garnier, Ernesto & Madlener, Reinhard, 2014. "Balancing Forecast Errors in Continuous-Trade Intraday Markets," FCN Working Papers 2/2014, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    4. Hirth, Lion & Ziegenhagen, Inka, 2015. "Balancing power and variable renewables: Three links," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1035-1051.
    5. Barthelmie, R.J. & Murray, F. & Pryor, S.C., 2008. "The economic benefit of short-term forecasting for wind energy in the UK electricity market," Energy Policy, Elsevier, vol. 36(5), pages 1687-1696, May.
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    1. Gandhi, Oktoviano & Zhang, Wenjie & Kumar, Dhivya Sampath & Rodríguez-Gallegos, Carlos D. & Yagli, Gokhan Mert & Yang, Dazhi & Reindl, Thomas & Srinivasan, Dipti, 2024. "The value of solar forecasts and the cost of their errors: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

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    Keywords

    Forecasting evaluation; renewable energy; electricity markets; balancing costs; artificial neural networks; clear sky model; Germany;
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