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Photovoltaic power forecasting using simple data-driven models without weather data


The present contribution offers evidence regarding the possibility of obtaining reasonable photovoltaic power forecasts without using weather data and with simple data-driven models. The lack of weather data as input stems from the fact that the constant obtainment of forecast weather data might become too expensive or that communication with weather services might fail, but still accurate planning and scheduling decisions have to be conducted. Therefore, accurate one-day ahead forecasting models with only information of past generated power as input for offline photovoltaic systems or as backup in case of communication failures are of interest. The results contained in the present contribution, obtained using a freely available dataset, provide a baseline with which more complex forecasting models can be compared. Additionally, it will also be shown that the presented weather-free data-driven models provide better forecasts than a trivial persistence technique for different forecast horizons. The methodology used in the present work for the data preprocessing and the creation and validation of forecasting models has a generalization capacity and thus can be used for different types of time series as well as different data mining techniques.

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  2. This filter can only be used during offline data analysis, due to its acausal nature.

  3. NaN: Not a Number.

  4. To search for reference time series, the PV power time series from all 300 households in the Ausgrid dataset are used.

  5. The floor operator rounds a real number to its preceding integer.


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The present contribution is supported by the Helmholtz Association under the Joint Initiative “Energy System 2050–A Contribution of the Research Field Energy”.

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Correspondence to Jorge Ángel González Ordiano.

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González Ordiano, J.Á., Waczowicz, S., Reischl, M. et al. Photovoltaic power forecasting using simple data-driven models without weather data. Comput Sci Res Dev 32, 237–246 (2017).

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  • Forecasting
  • Data-driven models
  • Photovoltaics
  • Weather-free
  • Energy Lab 2.0