“Dust in the wind...”, deep learning application to wind energy time series forecasting

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based on pure time series input. Deep learning is appearing as a new method that can be used for wind energy prediction. This work develops several deep learning architectures and shows their performance when applied to wind time series. The models have been tested with the most extensive wind dataset available, the National Renewable Laboratory Wind Toolkit, a dataset with 126,692 wind points in North America. The architectures designed are based on different approaches, Multi-Layer Perceptron Networks (MLP), Convolutional Networks (CNN), and Recurrent Networks (RNN). These deep learning architectures have been tested to obtain predictions in a 12-h ahead horizon, and the accuracy is measured with the coefficient of determination, the R² method. The application of the models to wind sites evenly distributed in the North America geography allows us to infer several conclusions on the relationships between methods, terrain, and forecasting complexity. The results show differences between the models and confirm the superior capabilities on the use of deep learning techniques for wind speed forecasting from wind time series data.Peer ReviewedPostprint (published version

Statistical Parameter Selection for Clustering Persistence Diagrams

International audienceIn urgent decision making applications, ensemble simulations are an important way to determine different outcome scenarios based on currently available data. In this paper, we will analyze the output of ensemble simulations by considering so-called persistence diagrams, which are reduced representations of the original data, motivated by the extraction of topological features. Based on a recently published progressive algorithm for the clustering of persistence diagrams, we determine the optimal number of clusters, and therefore the number of significantly different outcome scenarios, by the minimization of established statistical score functions. Furthermore, we present a proof-of-concept prototype implementation of the statistical selection of the number of clusters and provide the results of an experimental study, where this implementation has been applied to real-world ensemble data sets

Pacific Ocean Forcing and Atmospheric Variability are the Dominant Causes of Spatially Widespread Droughts in the Contiguous United States

The contributions of oceanic and atmospheric variability to spatially widespread summer droughts in the contiguous United States (hereafter, pan-CONUS droughts) are investigated using 16-member ensembles of the Community Climate Model version 3 (CCM3) forced with observed sea surface temperatures (SSTs) from 1856 to 2012. The employed SST forcing fields are either (i) global or restricted to the (ii) tropical Pacific or (iii) tropical Atlantic to isolate the impacts of these two ocean regions on pan-CONUS droughts. Model results show that SST forcing of pan-CONUS droughts originates almost entirely from the tropical Pacific because of atmospheric highs from the northern Pacific to eastern North America established by La Nia conditions, with little contribution from the tropical Atlantic. Notably, in all three model configurations, internal atmospheric variability influences pan-CONUS drought occurrence by as much or more than the ocean forcing and can alone cause pan-CONUS droughts by establishing a dominant high centered over the US montane West. Similar results are found for the Community Atmosphere Model version 5 (CAM5). Model results are compared to the observational record, which supports model-inferred contributions to pan-CONUS droughts from La Nias and internal atmospheric variability. While there may be an additional association with warm Atlantic SSTs in the observational record, this association is ambiguous due to the limited number of observed pan-CONUS. The ambiguity thus opens the possibility that the observational results are limited by sampling over the 20th-century and not at odds with the suggested dominance of Pacific Ocean forcing in the model ensembles

Progressive Wasserstein Barycenters of Persistence Diagrams

This paper presents an efficient algorithm for the progressive approximation of Wasserstein barycenters of persistence diagrams, with applications to the visual analysis of ensemble data. Given a set of scalar fields, our approach enables the computation of a persistence diagram which is representative of the set, and which visually conveys the number, data ranges and saliences of the main features of interest found in the set. Such representative diagrams are obtained by computing explicitly the discrete Wasserstein barycenter of the set of persistence diagrams, a notoriously computationally intensive task. In particular, we revisit efficient algorithms for Wasserstein distance approximation [12,51] to extend previous work on barycenter estimation [94]. We present a new fast algorithm, which progressively approximates the barycenter by iteratively increasing the computation accuracy as well as the number of persistent features in the output diagram. Such a progressivity drastically improves convergence in practice and allows to design an interruptible algorithm, capable of respecting computation time constraints. This enables the approximation of Wasserstein barycenters within interactive times. We present an application to ensemble clustering where we revisit the k-means algorithm to exploit our barycenters and compute, within execution time constraints, meaningful clusters of ensemble data along with their barycenter diagram. Extensive experiments on synthetic and real-life data sets report that our algorithm converges to barycenters that are qualitatively meaningful with regard to the applications, and quantitatively comparable to previous techniques, while offering an order of magnitude speedup when run until convergence (without time constraint). Our algorithm can be trivially parallelized to provide additional speedups in practice on standard workstations. [...

Principal Geodesic Analysis of Merge Trees (and Persistence Diagrams)

This paper presents a computational framework for the Principal Geodesic Analysis of merge trees (MT-PGA), a novel adaptation of the celebrated Principal Component Analysis (PCA) framework [87] to the Wasserstein metric space of merge trees [92]. We formulate MT-PGA computation as a constrained optimization problem, aiming at adjusting a basis of orthogonal geodesic axes, while minimizing a fitting energy. We introduce an efficient, iterative algorithm which exploits shared-memory parallelism, as well as an analytic expression of the fitting energy gradient, to ensure fast iterations. Our approach also trivially extends to extremum persistence diagrams. Extensive experiments on public ensembles demonstrate the efficiency of our approach - with MT-PGA computations in the orders of minutes for the largest examples. We show the utility of our contributions by extending to merge trees two typical PCA applications. First, we apply MT-PGA to data reduction and reliably compress merge trees by concisely representing them by their first coordinates in the MT-PGA basis. Second, we present a dimensionality reduction framework exploiting the first two directions of the MT-PGA basis to generate two-dimensional layouts of the ensemble. We augment these layouts with persistence correlation views, enabling global and local visual inspections of the feature variability in the ensemble. In both applications, quantitative experiments assess the relevance of our framework. Finally, we provide a lightweight C++ implementation that can be used to reproduce our results

Workshop on Drought Forecasting for Northeast Brazil

Precipitation forecasting parameters for northeast Brazil were developed. Hydrological, sociological, and economic aspects were examined. A drought forecasting model is presented

Report from solar physics

A discussion of the nature of solar physics is followed by a brief review of recent advances in the field. These advances include: the first direct experimental confirmation of the central role played by thermonuclear processes in stars; the discovery that the 5-minute oscillations of the Sun are a global seismic phenomenon that can be used as a probe of the structure and dynamical behavior of the solar interior; the discovery that the solar magnetic field is subdivided into individual flux tubes with field strength exceeding 1000 gauss. Also covered was a science strategy for pure solar physics. Brief discussions are given of solar-terrestrial physics, solar/stellar relationships, and suggested space missions