Wind energy has become one of the fastest growing renewable sources during last few decades. Sudden changes in wind power output, called wind ramps, recently attracted great research interest in wind power forecasting community. Conventional ramp prediction methods derive future ramp estimates from power forecast series. We suggest to analyze real wind power series or other weather parameters, searching for specific patterns and dependencies indicating forthcoming wind ramp events.This paper presents a methodology for parametric analysis of time series preceding wind ramp events. The presented methodology is based on probabilistic data analysis. Sensitivity of the developed algorithm can be adjusted through the size of the time window defined before a ramp event. Power production and other weather data are standardized, averaged and then searched for specific patterns in the form of trend lines. The resulting methodology does not require the use of a numerical weather prediction (NWP) model; this is a significant simplification compared to most conventional methods. The proposed methodology is tested using power production and wind speed data collected between August 2011 and July 2012 at a wind farm located in a wind-rich region of southern Alberta, Canada.

The use of a 3D range scanning device for autonomous object description or unknown environment mapping leads to the necessity of improving computer methods based on identical point pairs from different point clouds (so-called registration problem). The registration problem and three-dimensional transformation of coordinates still require further research. The paper attempts to guide the reader through the vast field of existing registration methods so that he can choose the appropriate approach for his particular problem. Furthermore, the article contains a regression method that enables the estimation of the covariance matrix of the transformation parameters and the calculation of the uncertainty of the estimated points. This makes it possible to extend existing registration methods with uncertainty estimates and to improve knowledge about the performed registration. The paper’s primary purpose is to present a survey of known methods and basic estimation theory concepts for the point cloud registration problem. The focus will be on the guiding principles of the estimation theory: ICP algorithm; Normal Distribution Transform; Feature-based registration; Iterative dual correspondences; Probabilistic iterative correspondence method; Point-based registration; Quadratic patches; Likelihood-field matching; Conditional random fields; Branch-and-bound registration; PointReg. The secondary purpose of this article is to show an innovative statistical model for this transformation problem. The new theory needs known covariance matrices of identical point coordinates. An unknown rotation matrix and shift vector have been estimated using a nonlinear regression model with nonlinear constraints. The paper ends with a relevant numerical example.

We introduce a new fuzzy linear regression method. The method is capable of approximating fuzzy relationships between an independent and a dependent variable. The independent and dependent variables are expected to be a real value and triangular fuzzy numbers, respectively. We demonstrate on twenty datasets that the method is reliable, and it is less sensitive to outliers, compare with possibilistic-based fuzzy regression methods. Unlike other commonly used fuzzy regression methods, the presented method is simple for implementation and it has linear time-complexity. The method guarantees non-negativity of model parameter spreads.

Nonsensitiveness regions for estimators of linear functions, for confidence ellipsoids, for the level of a test of a linear hypothesis on parameters and for the value of the power function are investi

During last few decades wind energy has become one of the fastest growing renewable sources. Recent research interest in wind power forecasting community is focused on wind ramps, sudden changes in wind power output. Conventional ramp prediction methods derive future ramp estimates from power forecast series. We suggest to analyze real wind power series or other weather parameters searching for specific patterns and dependencies signalizing forthcoming wind ramp event. In this paper, principal component analysis of wind speed time series is presented. Analyzed power production and wind speed data were collected from August 2011 to July 2012 on sample wind farm located close to Let bridge, AB Canada. Sensitivity of analysis is defined by length of selected time frame. Frequency tables for first three principal components are used to evaluate conditional probability of forthcoming wind ramp event. Advantage of proposed methodology compared to conventional methods is that numerical weather prediction (NWP) model producing wind forecasts is not required.