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This study presents a method for probabilistic forecasting of solar irradiance based on the joint probability distribution function (PDF) of irradiance predicted by numerical weather prediction (NWP) and irradiance observed. Multidimensional kernel density estimation was used to construct this joint PDF. The probabilistic forecast is obtained by deriving a conditional PDF given a current NWP by using the Bayes rule. The proposed method can naturally handle the nonlinear nature of the relation between observed and predicted irradiance. The method showed better statistical performance in terms of the continuous ranked probability score, reliability, and sharpness than an existing probabilistic forecasting method based on ensemble NWP. Simulation of solar power trading based on pricing of an actual electric power market also confirmed that the proposed method results in greater profit by suppressing penalties imposed due to overbidding.

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SummaryAs a result of the ongoing development of non-invasive analysis of brain function, detailed brain images can be obtained, from which the relations between brain areas and brain functions can be understood. The relations between brain areas and brain functions are described by rules. Knowledge discovery from functional brain images is knowledge discovery from pattern data, which is a new field different from knowledge discovery from symbolic data or numerical data. We have been developing a new method called Logical Regression Analysis. The Logical Regression Analysis consists of two steps. The first step is a regression analysis. The second step is rule extraction from the regression formula obtained by the regression analysis. In this paper, we apply the Logical Regression Analysis to functional brain images to discover relations between a brain function and brain areas. We use nonparametric regression analysis as a regression analysis, since there are not sufficient data to obtain linear formulas using conventional linear regression from functional brain images. Experimental results show that the algorithm works well for real data.

SUMMARYThere has recently been remarkable progress in noninvasive measurement of the brain (such as f-MRI, MEG, and PET), providing detailed functional brain images. Using this technique, the correspondence between the areas and functions of the brain can be precisely analyzed. Researchers in this field are now examining correspondences between areas and functions of the brain on the basis of functional brain images. The authors have been attempting to apply nonparametric regression analysis to f-MRI images, in an effort to develop an algorithm that identifies the areas of the brain automatically and objectively. Since the brain has a three-dimensional structure, it is desirable to apply three-dimensional nonparametric regression analysis, but that requires a tremendous amount of computation. The idea is applied to two-dimensional images as a first step. Satisfactory results are obtained compared to the results obtained by z-score, a typical technique; the results are reported in this paper.