SVR-Boosting ensemble model for electricity price forecasting in electric power market

A revised support vector regression (SVR) ensemble model based on boosting algorithm (SVR-Boosting) is presented in this paper for electricity price forecasting in electric power market. In the light of characteristics of electricity price sequence, a new triangular-shaped 为oss function is constructed in the training of the forecasting model to inhibit the learning from abnormal data in electricity price sequence. The results from actual data indicate that, compared with the single support vector regression model, the proposed SVR-Boosting ensemble model is able to enhance the stability of the model output remarkably, acquire higher predicting accuracy, and possess comparatively satisfactory generalization capability.

Study on Matching a 300 MVA Motor Generator with an Ohmic Heating Power Supply in HL-2M

A new 300 MVA/1350 MJ motor generator （MG） will be built to feed all of the poloidal field power supplies （PFPS） and auxiliary heating power supplies of the HL-2M tokamak. The MG has a vertical-shaft salient pole 6-phase synchronous generator and a coaxial 8500 kW induction motor. The Ohmic heating power supply （OHPS） consisting of 4-quadrant DC pulsed convertor is the one with the highest parameters among the PFPS. Therefore, the match between the generator and the OHPS is very important. The matching study with Matlab/Simulink is described in this paper. The simulation results show that the subtransient reactance of the generator is closely related to the inversion operation of the OHPS. By setting various subtransient reactance in the simulation generator model and considering the cost reduction, the optimized parameters are obtained as xd＂ = 0.405 p.u. at 100 Hz for the generator. The models built in the simulation can be used as an important tool for studying the dynamic characteristics and the control strategy of other HL-2M PFPSes.

ANALYSIS OF THE FIELDS SCATTERED FROMA TWO-DIMENSIONAL CYLINDER ILLUMINATED BY A POINT SOUTRE

Usually, a two-dimensional scattering problem for radar or other appli-cations means a cylindrical scatterer illuminated by a plane wave. But it is not suit-able for analysing the scattered fields in the case of EM exploration, in which the un-derground scatterer is illuminated by a three-dimensional emitter,or simply, apointsource. The latter has not yet been solved completely due to the infinitely extensionof the scatterer. In this papor, a hybrid method combining the GMT expanding incross-section and SDT for axial dimension is proposed, the numerical procedures areobviously simplified, especially for a larse crase-section. Various typical exaniplesincluding perfectly conducting and lossy dielectric cylinders with several differentcross-sections are given.

DUALITY OF GENERALIZED DUNKL-LIPSCHITZ SPACES

Abstract The aim of this paper is to prove duality and reflexivity of generalized Lipschitz spaces ∧κα,p,q（R）, α ∈R and 1≤〈 p, q ≤∞, in the context of Dunkl harmonic analysis.

ON APPROXIMATION PROPERTIES OF NON-CONVOLUTION TYPE NONLINEAR INTEGRAL OPERATORS——Dedicated to Professor Paul L. Butzer on his 80^(th) Birthday

In the present paper we state some approximation theorems concerning point- wise convergence and its rate for a class of non-convolution type nonlinear integral opera- tors of the form：Tλ（f;x）=B∫AKλ（t,x,f（t））dr,x∈〈a,b〉λλA.In particular, we obtain the pointwise convergence and its rate at some characteristic points x0 off as （x,λ） → （x0, λ0） in LI 〈A,B 〉, where 〈 a,b 〉 and 〈A,B 〉 are is an arbitrary intervals in R, A is a non-empty set of indices with a topology and X0 an accumulation point of A in this topology. The results of the present paper generalize several ones obtained previously in the papers [191-[23]

The Confirmed Validity of the Explanatory Aspect of the Thermohydrogravidynamic Theory Concerning the Evaluated Maximal Magnitude of the Strongest Earthquake of the Earth near the Predicted Date 2021.1 AD during the Range from October 27, 2020 to May 17, 2021 AD

We present the explanation (in the frame of the established thermohydrogravidynamic technology) of the maximal magnitude M = 8.1 (according to the U.S. Geological Survey) of the strongest earthquake of the Earth occurred in Kermadec Islands, New Zealand on March 4, 2021 AD (during the considered range from October 27, 2020 to May 17, 2021 AD). This strongest earthquake occurred near the calculated date 2021.1 AD corresponding (in the frame of the thermohydrogravidynamic theory) to the local maximal combined planetary and solar integral energy gravitational influence on the internal rigid core of the Earth. To obtain this explanation, we have analyzed the strongest earthquakes of the Earth (according to the U.S. Geological Survey) occurred near the dates of the local maximal combined planetary and solar integral energy gravitational influences on the internal rigid core of the Earth.

Optimal configuration of a class of endoreversible heat engines for maximum efficiency with radiative heat transfer law

Optimal configuration of a class of endoreversible heat engines with fixed duration,input energy and radiative heat transfer law (q∝Δ(T4)) is determined. The optimal cycle that maximizes the efficiency of the heat engine is obtained by using opti-mal-control theory,and the differential equations are solved by the Taylor series expansion. It is shown that the optimal cycle has eight branches including two isothermal branches,four maximum-efficiency branches,and two adiabatic branches. The interval of each branch is obtained,as well as the solutions of the temperatures of the heat reservoirs and the working fluid. A numerical example is given. The obtained results are compared with those obtained with the Newton’s heat transfer law for the maximum efficiency objective,those with linear phe-nomenological heat transfer law for the maximum efficiency objective,and those with radiative heat transfer law for the maximum power output objective.

Discrete-time delayed standard neural network model and its application

A novel neural network model, termed the discrete-time delayed standard neural network model （DDSNNM）, and similar to the nominal model in linear robust control theory, is suggested to facilitate the stability analysis of discrete-time recurrent neural networks （RNNs） and to ease the synthesis of controllers for discrete-time nonlinear systems. The model is composed of a discrete-time linear dynamic system and a bounded static delayed （or non-delayed） nonlinear operator. By combining various Lyapunov functionals with the S-procedure, sufficient conditions for the global asymptotic stability and global exponential stability of the DDSNNM are derived, which are formulated as linear or nonlinear matrix inequalities. Most discrete-time delayed or non-delayed RNNs, or discrete-time neural-network-based nonlinear control systems can be transformed into the DDSNNMs for stability analysis and controller synthesis in a unified way. Two application examples are given where the DDSNNMs are employed to analyze the stability of the discrete-time cellular neural networks （CNNs） and to synthesize the neuro-controllers for the discrete-time nonlinear systems, respectively. Through these examples, it is demonstrated that the DDSNNM not only makes the stability analysis of the RNNs much easier, but also provides a new approach to the synthesis of the controllers for the nonlinear systems.

Recursive design of nonlinear H _∞ excitation controller

This work is concerned with the problem of L2 gain disturbance attenuation for nonlinear systems and nonlinear robust control for power systems. In terms of the recurrence design approach proposed, the nonnegative solution of dissipative inequality and the storage function of nonlinear H∞ control for a generator excitation system are acquired. From this storage function, the excitation controller is constructed. Moreover, simulation results manifest the effectiveness of this design method.

Low-Dimensional SIR Epidemic Models with Demographics on Heterogeneous Networks

To investigate the impacts of demographics on the spread of infectious diseases, a susceptib- le-infectious-recovered （SIR） pairwise model on heterogeneous networks is established. This model is reduced by using the probability generating function and moment closure approximations. The basic reproduction number of the low-dimensional model is derived to rely on the recruitment and death rate, the first and second moments of newcomers＇ degree distribution. Sensitivity analysis for the basic reproduction number is performed, which indicates that a larger variance of newcomers＇ degrees can lead to an epidemic outbreak with a smaller transmission rate, and contribute to a slight decrease of the final density of infectious nodes with a larger transmission rate. Besides, stochastic simulations indicate that the low-dimensional model based on the log-normal moment closure assumption can well capture important properties of an epidemic. And the authors discover that a larger recruitment rate can inhibit the spread of disease.

L_1 regression estimate and its bootstrap

We study the asymptotic distribution of the L1 regression estimator under general condi-tions with matrix norming and possibly non i.i.d.errors.We then introduce an appropriate bootstrap procedure to estimate the distribution of this estimator and study its asymptotic properties.It is shown that this bootstrap is consistent under suitable conditions and in other situations the bootstrap limit is a random distribution.

Generalized Intersection Bodies with Parameter

In 2005, the classical intersection bodies and L_p intersection bodies were extended. Afterwards, the concept of gen-eral L_p intersection bodies and the generalized intersection bodies was introduced. In this paper, we define the generalized bodies with parameter. Besides, we establish the extremal values for volume, Brunn-Minkowski type inequality for radial combination and L_p harmonic Blaschke combination of this notion.