Hourly Solar Irradiation Forecast Using Hybrid Local Gravitational Clustering and Group Method of Data Handling Methods

The foundation for many solar energies uses as well as economic and environmental concerns is global solar irradiation information. However, due to solar irradiation and measurements variations, reliable worldwide statistics on solar irradiation are frequently impossible or difficult to acquire. In addition, more precise forecast of solar irradiation plays an increasingly important role in electric energy planning and management due to integrating photovoltaic solar systems into power networks. Hence, this paper proposes a new hybrid model for 1-hour ahead solar irradiation forecasting called LGC- GMDH (local gravitational clustering- Group method of data handling). The novel LGC- GMDH model is based on the local clustering that adequately captures the underlying features of the solar irradiation time series. Each cluster is then forecasted using the GMDH method, which is a self-organized system that is capable of handling very complicated nonlinear problems. Finally, these local forecasts are reconstructed in order to obtain the global forecast. Comparative study between the proposed model and the traditional individual models such as; backpropagation neural network (BP), supporting vector machines (SVM), long short-term memory (LTSM), hybrid models such; BP-MLP, RNN-MLP, LSTM-MLP hybrid wavelet packet decomposition (WPD), convolutional neural network (CNN) with LSTM-MLP, and ANFIS clustering shows that the proposed model overcomes conventional model deficiencies and achieves more precise predicting outcome.

An Improved Gravitational Clustering Based on Local Density

Gravitational clustering algorithm (Gravc) is a novel and excellent dynamic clustering algorithm that can accurately cluster complex dataset with arbitrary shape and distribution. However, high time complexity is a key challenge to the gravitational clustering algorithm. To solve this problem, an improved gravitational clustering algorithm based on the local density is proposed in this paper, called FastGravc. The main contributions of this paper are as follows. First of all, a local density-based data compression strategy is designed to reduce the number of data objects and the number of neighbors of each object participating in the gravitational clustering algorithm. Secondly, the traditional gravity model is optimized to adapt to the quality differences of different objects caused by data compression strategy. And then, the improved gravitational clustering algorithm FastGravc is proposed by integrating the above optimization strategies. Finally, extensive experimental results on synthetic and real-world datasets verify the effectiveness and efficiency of FastGravc algorithm.

Fractons from frustration in hole-doped antiferromagnets

Recent theoretical research on tensor gauge theories led to the discovery of an exotic type of quasiparticles, dubbed fractons, that obey both charge and dipole conservation. Here we describe physical implementation of dipole conservation laws in realistic systems. We show that fractons find a natural realization in hole-doped antiferromagnets. There, individual holes are largely immobile, while dipolar hole pairs move with ease. First, we demonstrate a broad parametric regime of fracton behavior in hole-doped two-dimensional Ising antiferromagnets viable through five orders in perturbation theory. We then specialize to the case of holes confined to one dimension in an otherwise two-dimensional antiferromagnetic background, which can be realized via the application of external fields in experiments, and prove ideal fracton behavior. We explicitly map the model onto a fracton Hamiltonian featuring conservation of dipole moment. Manifestations of fractonicity in these systems include gravitational clustering of holes. We also discuss diagnostics of fracton behavior, which we argue is borne out in existing experimental results.

Quantifying the redshift space distortion of the bispectrum II: induced non-Gaussianity at second-order perturbation

ABSTRACT The anisotrpy of the redshift space bispectrum $B^s(\boldsymbol {k_1},\boldsymbol {k_2},\boldsymbol {k_3})$, which contains a wealth of cosmological information, is completely quantified using multipole moments $\bar{B}^m_{\ell }(k_1,\mu ,t)$, where k1, the length of the largest side, and (μ, t), respectively, quantify the size and the shape of the triangle $(\boldsymbol {k_1},\boldsymbol {k_2},\boldsymbol {k_3})$. We present analytical expressions for all the multipoles that are predicted to be non-zero (ℓ ≤ 8, m ≤ 6) at second-order perturbation theory. The multipoles also depend on β1, b1, and γ2, which quantify the linear redshift distortion parameter, linear bias and quadratic bias, respectively. Considering triangles of all possible shapes, we analyse the shape dependence of all of the multipoles holding $k_1=0.2 \, {\rm Mpc}^{-1}, \beta _1=1, b_1=1$, and γ2 = 0 fixed. The monopole $\bar{B}^0_0$, which is positive everywhere, is minimum for equilateral triangles. $\bar{B}_0^0$ increases towards linear triangles, and is maximum for linear triangles close to the squeezed limit. Both $\bar{B}^0_{2}$ and $\bar{B}^0_4$ are similar to $\bar{B}^0_0$, however, the quadrupole $\bar{B}^0_2$ exceeds $\bar{B}^0_0$ over a significant range of shapes. The other multipoles, many of which become negative, have magnitudes smaller than $\bar{B}^0_0$. In most cases, the maxima or minima, or both, occur very close to the squeezed limit. $\mid \bar{B}^m_{\ell } \mid$ is found to decrease rapidly if ℓ or m are increased. The shape dependence shown here is characteristic of non-linear gravitational clustering. Non-linear bias, if present, will lead to a different shape dependence.

Constraining cosmology with big data statistics of cosmological graphs

ABSTRACT By utilizing large-scale graph analytic tools implemented in the modern big data platform, apache spark, we investigate the topological structure of gravitational clustering in five different universes produced by cosmological N-body simulations with varying parameters: (1) a WMAP 5-yr compatible ΛCDM cosmology, (2) two different dark energy equation of state variants, and (3) two different cosmic matter density variants. For the big data calculations, we use a custom build of standalone Spark/Hadoop cluster at Korea Institute for Advanced Study and Dataproc Compute Engine in Google Cloud Platform with sample sizes ranging from 7 to 200 million. We find that among the many possible graph-topological measures, three simple ones: (1) the average of number of neighbours (the so-called average vertex degree) α, (2) closed-to-connected triple fraction (the so-called transitivity) $\tau _\Delta$, and (3) the cumulative number density ns ≥ 5 of subgraphs with connected component size s ≥ 5, can effectively discriminate among the five model universes. Since these graph-topological measures are directly related with the usual n-points correlation functions of the cosmic density field, graph-topological statistics powered by big data computational infrastructure opens a new, intuitive, and computationally efficient window into the dark Universe.