- Volume 13 Issue 4
DOI QR Code
Prediction Intervals for Day-Ahead Photovoltaic Power Forecasts with Non-Parametric and Parametric Distributions
- Fonseca, Joao Gari da Silva Junior (The University of Tokyo, Institute of Industrial Science) ;
- Ohtake, Hideaki (National Institute of Advanced Industrial Science and Technology, Research Center for Photovoltaics) ;
- Oozeki, Takashi (National Institute of Advanced Industrial Science and Technology, Research Center for Photovoltaics) ;
- Ogimoto, Kazuhiko (The University of Tokyo, Institute of Industrial Science)
- Received : 2017.04.27
- Accepted : 2017.12.14
- Published : 2018.07.01
The objective of this study is to compare the suitability of a non-parametric and 3 parametric distributions in the characterization of prediction intervals of photovoltaic power forecasts with high confidence levels. The prediction intervals of the forecasts are calculated using a method based on recent past data similar to the target forecast input data, and on a distribution assumption for the forecast error. To compare the suitability of the distributions, prediction intervals were calculated using the proposed method and each of the 4 distributions. The calculations were done for one year of day-ahead forecasts of hourly power generation of 432 PV systems. The systems have different sizes and specifications, and are installed in different locations in Japan. The results show that, in general, the non-parametric distribution assumption for the forecast error yielded the best prediction intervals. For example, with a confidence level of 85% the use of the non-parametric distribution assumption yielded a median annual forecast error coverage of 86.9%. This result was close to the one obtained with the Laplacian distribution assumption (87.8% of coverage for the same confidence level). Contrasting with that, using a Gaussian and Hyperbolic distributions yielded median annual forecast error coverage of 89.5% and 90.5%.
Photovoltaic power;Day-ahead forecasting;Forecast error;Prediction intervals;Maximum likelihood estimation;Parametric versus non-parametric distributions
Grant : Research and Development of PV Performance and Reliability Characterization Technologies
Supported by : NEDO
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