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The development of a methodology to forecast accurately the power produced by photovoltaic systems can be an important tool for the dissemination and integration of such systems on the public electricity grids. Thus, the objective of this study was to forecast the power production of a 1‐MW photovoltaic power plant in Kitakyushu, Japan, using a new methodology based on support vector machines and on the use of several numerically predicted weather variables, including cloudiness. Hourly forecasts of the power produced for 1 year were carried out. Moreover, the effect of the use of numerically predicted cloudiness on the quality of the forecasts was also investigated. The forecasts of power production obtained with the proposed methodology had a root mean square error of 0.0948 MW h and a mean absolute error of 0.058 MW h. It was also found that the forecast and measured values of power production had a good level of correlation varying from 0.8 to 0.88 according to the season of the year. Finally, the use of numerically predicted cloudiness had an important role in the accuracy of the forecasts, and when cloudiness was not used, the root mean square error of the forecasts increased more than 32%, and the mean absolute error increased more than 42%. Copyright © 2011 John Wiley & Sons, Ltd.

The development of methods to forecast photovoltaic (PV) power generation regionally is of utmost importance to support the spread of such power systems in current power grids. The objective of this study is to propose and to evaluate methods to forecast regional PV power 1 day ahead of time and to compare their performances. Four forecast methods were regarded, of which two are new ones proposed in this study. Together, they characterize a set of forecast methods that can be applied in different scenarios regarding availability of data and infrastructure to make the forecasts. The forecast methods were based on the use of support vector regression and weather prediction data. Evaluations were performed for 1 year of hourly forecasts using data of 273 PV systems installed in two adjacent regions in Japan, Kanto, and Chubu. The results show the importance of selecting the proper forecast method regarding the region characteristics. For Chubu, the region with a variety of weather conditions, the forecast methods based on single systems' forecasts and the one based on stratified sampling provided the best results. In this case, the best annual normalized root mean square error (RMSE) and mean absolute error (MAE) were 0.25 and 0.15 kWh/kWhavg, respectively. For Kanto, with homogeneous weather conditions, the four methods performed similarly. In this case, the lowest annual forecast errors were 0.33 kWh/kWhavg for the normalized RMSE and 0.202 kWh/kWhavg for the normalized MAE. Copyright © 2014 John Wiley & Sons, Ltd.

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-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.

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