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Joao Gari da Silva Fonseca Jun,Takashi Oozeki,Hideaki Ohtake,Takumi Takashima,Ogimoto Kazuhiko 대한전기학회 2015 Journal of Electrical Engineering & Technology Vol.10 No.3
The objective of this study is to propose a method to calculate prediction intervals for oneday-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.
Joao Gari da Silva, Fonseca Jr.,Oozeki, Takashi,Ohtake, Hideaki,Shimose, Ken-ichi,Takashima, Takumi,Ogimoto, Kazuhiko The Korean Institute of Electrical Engineers 2013 The Journal of International Council on Electrical Vol.3 No.2
The objective of this study is to analyze 3 approaches to objectively set the configuration parameters of a support vector regression algorithm to forecast insolation. The approaches were based on techniques such as multifold cross-validation, grid search, and frequency analysis. The ${\nu}$ support vector regression with a Gauss function as kernel function was used to forecast insolation. The configuration parameters set were the cost parameter, the gamma parameter in the kernel function, and the ${\nu}$ parameter. The analysis was done using weather related data of Tsukuba city in Japan, from January to December of 2009. The results show that variations of the forecast errors caused by using the different approaches were small, 4.5% in the worst case. Moreover, any of the proposed approaches yielded forecasts of insolation with annual root mean square errors lower than $0.12kWh/m^2$ and mean absolute errors lower than $0.07kWh/m^2$, what shows their applicability.
Fonseca Junior, Joao Gari da Silva,Oozeki, Takashi,Ohtake, Hideaki,Takashima, Takumi,Kazuhiko, Ogimoto The Korean Institute of Electrical Engineers 2015 Journal of Electrical Engineering & Technology Vol.10 No.3
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.
Joao Gari da Silva Fonseca Jun,Hideaki Ohtake,Takashi Oozeki,Kazuhiko Ogimoto 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.4
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%.
Fonseca, Joao Gari da Silva Junior,Ohtake, Hideaki,Oozeki, Takashi,Ogimoto, Kazuhiko The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.4
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%.