Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

The mass distribution of Galactic double neutron stars

The conventional wisdom, dating back to 2012, is that the mass distribution of Galactic double neutron stars is well-fit by a Gaussian distribution with a mean of $1.33 M_\odot$ and a width of $0.09 M_\odot$. With the recent discovery of new Galactic double neutron stars and GW170817, the first neutron star merger event to be observed with gravitational waves, it is timely to revisit this model. In order to constrain the mass distribution of double neutron stars, we perform Bayesian inference using a sample of 17 Galactic double neutron stars effectively doubling the sample used in previous studies. We expand the space of models so that the recycled neutron star need not be drawn from the same distribution as the non-recycled companion. Moreover, we consider different functional forms including uniform, single-Gaussian, and two-Gaussian distributions. While there is insufficient data to draw firm conclusions, we find positive support (a Bayes factor of 9) for the hypothesis that recycled and non-recycled neutron stars have distinct mass distributions. The most probable model---preferred with a Bayes factor of 29 over the conventional model---is one in which the recycled neutron star mass is distributed according to a two-Gaussian distribution and the non-recycled neutron star mass is distributed uniformly. We show that precise component mass measurements of $\approx 20$ double neutron stars are required in order to determine with high confidence (a Bayes factor of 150) if recycled and non-recycled neutron stars come from a common distribution. Approximately $60$ are needed in order to establish the detailed shape of the distributions.Comment: Minor update of PSR J1913+1102 masses, 13 pages, 7 figures, 5 table

Scaling in the distribution of intertrade durations of Chinese stocks

The distribution of intertrade durations, defined as the waiting times between two consecutive transactions, is investigated based upon the limit order book data of 23 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. A scaling pattern is observed in the distributions of intertrade durations, where the empirical density functions of the normalized intertrade durations of all 23 stocks collapse onto a single curve. The scaling pattern is also observed in the intertrade duration distributions for filled and partially filled trades and in the conditional distributions. The ensemble distributions for all stocks are modeled by the Weibull and the Tsallis $q$-exponential distributions. Maximum likelihood estimation shows that the Weibull distribution outperforms the $q$-exponential for not-too-large intertrade durations which account for more than 98.5% of the data. Alternatively, nonlinear least-squares estimation selects the $q$-exponential as a better model, in which the optimization is conducted on the distance between empirical and theoretical values of the logarithmic probability densities. The distribution of intertrade durations is Weibull followed by a power-law tail with an asymptotic tail exponent close to 3.Comment: 16 elsart pages including 3 eps figure

Ferromagnetic to antiferromagnetic transition of one-dimensional spinor Bose gases with spin-orbit coupling

We have analytically solved one-dimensional interacting two-component bosonic gases with spin-orbit (SO) coupling by the Bethe-ansatz method. Through a gauge transformation, the effect of SO coupling is incorporated into a spin-dependent twisted boundary condition. Our result shows that the SO coupling can influence the eigenenergy in a periodical pattern. The interplay between interaction and SO coupling may induce the energy level crossing for the ground state, which leads to a transition from the ferromagnetic to antiferromagnetic state.Comment: 6 pages, 4 figure

Tests of homogeneity of several location and scale populations, and analysis of paired count data with zero-inflation and over-dispersion.

This thesis consists of two parts, referred as Part I and Part II. Part I. Testing homogeneity of several location-scale populations. The widely used method for testing homogeneity of several normal populations is to test the equality of means based on the assumption that the variances among different groups are same. But in practice, we often get data which are different not only in means but also in variances. Singh (1986) tests the homogeneity of several normal populations simultaneously regarding commonality of means and variances based on a method by Fisher (1950). However, this problem arises not only in normal populations but also in other populations. In this thesis, I extend Fisher\u27s method to location-scale models in general. The location-scale models encompass all two parameter mean-variance models, such as the normal, negative binomial and beta-binomial models. Two test statistics are developed, one of which is based on the combination of two likelihood ratio statistics and the other is based on the combination of two score test statistics. Theoretical and empirical properties of these procedures are studied and applied to real life data analysis problems. Part II. Analysis of paired count data with zero-inflation and over-dispersion. Data in the form of paired counts (pre-treatment and post-treatment counts) arise in many fields such as biomedical, toxicology, epidemiology and so on. Poisson and binomial models are the most widely used models for these data. Frequently encountered problems in these data are the presence of extra-zeros and extra-dispersion and, the possible correlation between the pre-treatment and post-treatment count. In this thesis I developed methods of analysis for two different sets of paired count data, one of the data set is obtained from an experiment on premature ventricular contractions (PVC) (Berry, 1987) and the other set is a dental epidemiology data representing decayed, missing and filled teeth (DMFT) index (Bohning, Dietz, Schlattmann, Mendonca and Kirchner, 1999). I then study properties of these methods and analyse the PVC data and the DMFT index data.Dept. of Mathematics and Statistics. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .J53. Source: Dissertation Abstracts International, Volume: 65-10, Section: B, page: 5219. Adviser: S. R. Paul. Thesis (Ph.D.)--University of Windsor (Canada), 2004