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

Characterization of four-qubit states via Bell inequalities

A set of Bell inequalities classifying the quantum entanglement of four-qubit states is presented. These inequalities involve only two measurement settings per observer and can characterize fully separable, bi-separable and tri-separable quantum states. In addition, a quadratic inequality of the Bell operators for four-qubit systems is derived

Quark number scaling of hadronic pTp_T spectra and constituent quark degree of freedom in pp-Pb collisions at sNN=5.02\sqrt{s_{NN}}=5.02 TeV

We show that the experimental data of $p_T$ spectra of identified hadrons released recently by ALICE collaboration for $p$-Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV exhibit a distinct universal behavior --- the quark number scaling. We further show that the scaling is a direct consequence of quark (re-)combination mechanism of hadronization and can be regarded as a strong indication of the existence of the underlying source with constituent quark degree of freedom for the production of hadrons in $p$-Pb collisions at such high energies. We make also predictions for production of other hadrons.Comment: 5 pages, 3 figure

Precise QCD predictions on top quark pair production mediated by massive color octet vector boson at hadron colliders

We present a theoretical framework for systematically calculating next-to-leading order (NLO) QCD effects to various experimental observables in models with massive COVB in a model independent way at hadron colliders. Specifically, we show the numerical results for the NLO QCD corrections to total cross sections, invariant mass distribution and AFB of top quark pairs production mediated by a massive COVB in both the fixed scale (top quark mass) scheme and the dynamical scale (top pair invariant mass) scheme. Our results show that the NLO QCD calculations in the dynamical scale scheme is more reasonable than the fixed scheme and the naive estimate of the NLO effects by simple rescaling of the LO results with the SM NLO K-factor is not appropriate.Comment: 6 pages, 5 figures, 2 tables; version published in EPJ