Using Conditional Kernel Density Estimation for Wind Power Density Forecasting
نویسندگان
چکیده
منابع مشابه
Using Conditional Kernel Density Estimation for Wind Power Density Forecasting
Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this paper, we develop an approach to producing dens...
متن کاملShared kernel models for class conditional density estimation
We present probabilistic models which are suitable for class conditional density estimation and can be regarded as shared kernel models where sharing means that each kernel may contribute to the estimation of the conditional densities of an classes. We first propose a model that constitutes an adaptation of the classical radial basis function (RBF) network (with full sharing of kernels among cl...
متن کاملProbabilistic short-term wind power forecasting based on kernel density estimators
Short-term wind power forecasting tools have been developed for some time. The majority of such tools usually provide single-valued (spot) predictions. Such predictions are however often not adequate when the aim is decision-making under uncertainty. In that case there is a clear requirement by end-users to have additional information on the uncertainty of the predictions for performing efficie...
متن کاملA geometric framework for density estimation and conditional density estimation
We introduce a geometrically intuitive procedure to obtain an estimator for a probability density function in the absence or presence of predictors. The estimation procedure is based on starting with an initial estimate of the density shape and then transforming it via a warping function to obtain the final estimate. The idea is to design the initial estimate to be computationally fast, albeit ...
متن کاملBottleneck Conditional Density Estimation
We propose a neural network framework for high-dimensional conditional density estimation. The Bottleneck Conditional Density Estimator (BCDE) is a variant of the conditional variational autoencoder (CVAE) that employs layer(s) of stochastic variables as the bottleneck between the input x and target y, where both are highdimensional. The key to effectively train BCDEs is the hybrid blending of ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2012
ISSN: 0162-1459,1537-274X
DOI: 10.1080/01621459.2011.643745