Data Science and Big Data in Energy Forecasting

This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, which was published at MDPI’s Energies journal. The special issue took place in 2017 and accepted a total of 13 papers from 7 different countries. Electrical, solar and wind energy forecasting were the most analyzed topics, introducing new methods with applications of utmost relevance.Ministerio de Competitividad TIN2014-55894-C2-RMinisterio de Competitividad TIN2017-88209-C2-

Energy landscape analysis of neuroimaging data

Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analyzed, and the data length.Comment: 22 pages, 4 figures, 1 tabl

Energy-minimizing two black holes initial data

An attempt to construct the ``ground state'' vacuum initial data for the gravitational field surrounding two black holes is presented. The ground state is defined as the gravitational initial data minimizing the ADM mass within the class of data for which the masses of the holes and their distance are fixed. To parameterize different geometric arrangements of the two holes (and, therefore, their distance) we use an appropriately chosen scale factor. A method for analyzing the variations of the ADM mass and the masses (areas) of the horizons in terms of gravitational degrees of freedom is proposed. The Misner initial data are analyzed in this context: it is shown that they do not minimize the ADM mass.Comment: Minor corrections, 2 references adde

Nonparametric Dark Energy Reconstruction from Supernova Data

Understanding the origin of the accelerated expansion of the Universe poses one of the greatest challenges in physics today. Lacking a compelling fundamental theory to test, observational efforts are targeted at a better characterization of the underlying cause. If a new form of mass-energy, dark energy, is driving the acceleration, the redshift evolution of the equation of state parameter w(z) will hold essential clues as to its origin. To best exploit data from observations it is necessary to develop a robust and accurate reconstruction approach, with controlled errors, for w(z). We introduce a new, nonparametric method for solving the associated statistical inverse problem based on Gaussian Process modeling and Markov chain Monte Carlo sampling. Applying this method to recent supernova measurements, we reconstruct the continuous history of w out to redshift z=1.5.Comment: 4 pages, 2 figures, accepted for publication in Physical Review Letter

Chiral low-energy constants from tau data

We analyze how the recent precise hadronic tau-decay data on the V-A spectral function and general properties of QCD such as analyticity, the operator product expansion and chiral perturbation theory (ChPT), can be used to improve the knowledge of some of the low-energy constants of ChPT. In particular we find the most precise values of L_{9,10} (or equivalently l_{5,6}) at order p^4 and p^6 and the first phenomenological determination of C_87 (c_50).Comment: Proceedings of the 6th International Workshop on Chiral Dynamics (Bern, Switzerland, July 6-10, 2009). 9 pages, 3 figure

Interacting Energy Components and Observational H(z)H(z) Data

In this note, we extend our previous work [Phys. Lett. B 644, 7 (2007), astro-ph/0609597], and compare eleven interacting dark energy models with different couplings to the observational $H(z)$ data. However, none of these models is better than the simplest $\Lambda$CDM model. This implies that either more exotic couplings are needed in the cosmological models with interaction between dark energy and dust matter, or {\em there is no interaction at all}. We consider that this result is disadvantageous to the interacting dark energy models studied extensively in the literature.Comment: 15 pages, 5 figures, 3 tables, Latex2e; v2: references added; v3: discussions added, Phys. Lett. B in press; v4: published versio

Combined energy -- diffraction data refinement of decagonal AlNiCo

We incorporate realistic pair potential energies directly into a non-linear least-square fit of diffraction data to quantitatively compare structure models with experiment for the Ni-rich $d$(AlNiCo) quasicrystal. The initial structure models are derived from a few {\it a priori} assumptions (gross features of the Patterson function) and the pair potentials. In place of the common hyperspace approach to the structure refinement of quasicrystals, we use a real-space tile decoration scheme, which does not rely on strict quasiperiodicity, and makes it easy to enforce sensible local arrangements of the atoms. Inclusion of the energies provides information complementary to the diffraction data and protects the fit procedure from converging on spurious solutions. The method pinpoints sites which are likely to break the symmetry of their local environment.Comment: 7 pages, 5 figures, proceedings of the Internation Conference on Quasicrystals, Bangalore, India, August 200