Efficient Prediction Designs for Random Fields

For estimation and predictions of random fields it is increasingly acknowledged that the kriging variance may be a poor representative of true uncertainty. Experimental designs based on more elaborate criteria that are appropriate for empirical kriging are then often non-space-filling and very costly to determine. In this paper, we investigate the possibility of using a compound criterion inspired by an equivalence theorem type relation to build designs quasi-optimal for the empirical kriging variance, when space-filling designs become unsuitable. Two algorithms are proposed, one relying on stochastic optimization to explicitly identify the Pareto front, while the second uses the surrogate criteria as local heuristic to chose the points at which the (costly) true Empirical Kriging variance is effectively computed. We illustrate the performance of the algorithms presented on both a simple simulated example and a real oceanographic dataset

A criterion and incremental design construction for simultaneous kriging predictions

In this paper, we further investigate the problem of selecting a set of design points for universal kriging, which is a widely used technique for spatial data analysis. Our goal is to select the design points in order to make simultaneous predictions of the random variable of interest at a finite number of unsampled locations with maximum precision. Specifically, we consider as response a correlated random field given by a linear model with an unknown parameter vector and a spatial error correlation structure. We propose a new design criterion that aims at simultaneously minimizing the variation of the prediction errors at various points. We also present various efficient techniques for incrementally buillding designs for that criterion scaling well for high dimensions. Thus the method is particularly suitable for big data applications in areas of spatial data analysis such as mining, hydrogeology, natural resource monitoring, and environmental sciences or equivalently for any computer simulation experiments. The effectiveness of the proposed designs is demonstrated through numerical examples

The Anemos Wind Power forecasting Platform technology - techniques and experiences

Disponible : http://www.ewec2006proceedings.info/allfiles2/966_Ewec2006fullpaper.pdfInternational audienceIn the framework of the Anemos project we developed a professional, flexible platform for operating wind power prediction models, laying the main focus on state-of-the-art IT techniques, inter-platform operability, availability and safety of operation. Currently, 7 plug-in prediction models from all over Europe are able to work on this platform

Propose a comparison method of the PV variability based on roof-top PV solar data of Australia

© 2018 International Journal of Renewable Energy Research. The use of renewable sources of energy is rising in Australia, and with solar energy becoming the most dominant; the solar (PV) roof-top plant penetration in the electrical energy distribution grid is increasing. As Australia is the sixth largest country in the world consisting of a diverse range of climates, this may be a concern to Distribution Service Operators (DSOs) as the variability in PV power output in different areas, climates/weather and even time of day. This means that DSOs are required to quantify these 'uncertainties' for different zones in Australia to aid in the energy planning. This paper will examine PV variability metrics to identify suitable PV variable metric based on purpose of application and propose a method to compare PV variability of large cities in Australia based on historical roof-top PV solar data. This proposed method examined variability metrics and find out suitable variability metric based on purpose of application. The comparative study shows that the PV variability and the amount of smoothing are not equal at all the distribution area in Australia and varies with geographical climatic scenario