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On the Use of Maximum Likelihood and Input Data Similarity to Obtain Prediction Intervals for Forecasts of Photovoltaic Power Generation
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 Title & Authors
On the Use of Maximum Likelihood and Input Data Similarity to Obtain Prediction Intervals for Forecasts of Photovoltaic Power Generation
Fonseca Junior, Joao Gari da Silva; Oozeki, Takashi; Ohtake, Hideaki; Takashima, Takumi; Kazuhiko, Ogimoto;
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 Abstract
The objective of this study is to propose a method to calculate prediction intervals for one-day-ahead hourly forecasts of photovoltaic power generation and to evaluate its performance. One year of data of two systems, representing contrasting examples of forecast’ accuracy, were used. The method is based on the maximum likelihood estimation, the similarity between the input data of future and past forecasts of photovoltaic power, and on an assumption about the distribution of the error of the forecasts. Two assumptions for the forecast error distribution were evaluated, a Laplacian and a Gaussian distribution assumption. The results show that the proposed method models well the photovoltaic power forecast error when the Laplacian distribution is used. For both systems and intervals calculated with 4 confidence levels, the intervals contained the true photovoltaic power generation in the amount near to the expected one.
 Keywords
Photovoltaic power generation;One-day-ahead forecasts;Prediction intervals;Maximum likelihood estimation;Support vector regression;
 Language
English
 Cited by
1.
Ensemble forecasting of solar irradiance by applying a mesoscale meteorological model, Solar Energy, 2016, 136, 597  crossref(new windwow)
2.
Review of photovoltaic power forecasting, Solar Energy, 2016, 136, 78  crossref(new windwow)
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