Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series

Theoretic analysis shows that the output power of the distributed generation system is nonlinear and chaotic. And it is coupled with the microenvironment meteorological data. Chaos is an inherent property of nonlinear dynamic system. A predicator of the output power of the distributed generation system is to establish a nonlinear model of the dynamic system based on real time series in the reconstructed phase space. Firstly, chaos should be detected and quantified for the intensive studies of nonlinear systems. If the largest Lyapunov exponent is positive, the dynamical system must be chaotic. Then, the embedding dimension and the delay time are chosen based on the improved C-C method. The attractor of chaotic power time series can be reconstructed based on the embedding dimension and delay time in the phase space. By now, the neural network can be trained based on the training samples, which are observed from the distributed generation system. The neural network model will approximate the curve of output power adequately. Experimental results show that the maximum power point of the distributed generation system will be predicted based on the meteorological data. The system can be controlled effectively based on the prediction

Decomposition of a Multiobjective Optimization Problem Into a Number of Simple Multiobjective Subproblems

This letter suggests an approach for decomposing a multiobjective optimization problem (MOP) into a set of simple multiobjective optimization subproblems. Using this approach, it proposes MOEA/D-M2M, a new version of multiobjective optimization evolutionary algorithm-based decomposition. This proposed algorithm solves these subproblems in a collaborative way. Each subproblem has its own population and receives computational effort at each generation. In such a way, population diversity can be maintained, which is critical for solving some MOPs. Experimental studies have been conducted to compare MOEA/D-M2M with classic MOEA/D and NSGA-II. This letter argues that population diversity is more important than convergence in multiobjective evolutionary algorithms for dealing with some MOPs. It also explains why MOEA/D-M2M performs better. © 2013 IEEE

Modeling of the aerosol-cloud interactions in marine stratocumulus.

In order to study the cloud processing of aerosols, a two-parameter drop spectrum that depends on both the drop mass and the solute mass has been introduced into the CIMMS LES model. The aerosol processing due to condensation, cloud droplet collision-coalescence and drizzle fallout have been studied through model simulation. The results show that continental (polluted) air can be modified to marine air within approximately 2 days through reduction of the aerosol number concentration. Coalescence is responsible for nearly all of the cloud condensation nuclei (CCN) number reduction. (Abstract shortened by UMI.)Using the CIMMS LES model with the VO method for condensation calculation, the aerosol effects on cloud microphysics and cloud radiative properties through the simulation of ship track formation have been studied. The CIMMS LES model has been run using both bulk and explicit microphysics to study ship track formation under various boundary layer conditions. Using a bulk thermodynamic formulation, I contrast the rates of effluent transport through a well-mixed boundary layer and through a decoupled layer. I also simulate the effects of heat injected by the ship engine exhaust on the transport of ship effluents into the cloud layer, finding a significant effect. Using an explicit microphysical model, I carry out simulations in clean and relatively polluted air. I find that a ship track forms easily in a well-mixed convective boundary layer in an environment with low cloud condensation nuclei, but its formation may be suppressed by the stable transition layer in the decoupled case. I also find that a ship track survives longer in a clean boundary layer than in a polluted environment.This dissertation is primarily focused on the study of aerosol-cloud interactions. First, the CIMMS Large Eddy Simulation (LES) model is used to study the effects of aerosol on cloud microstructure and cloud radiative properties by modeling the ship track effects. Second, a new enhanced version of the CIMMS explicit microphysical model is developed. The model allows us to track aerosol particles during their interactions with cloud and is used to simulate cloud processing of aerosols. An important part of the research was the development of a new variational optimization (VO) method which significantly limits the artificial broadening of cloud drop spectrum in the condensation calculations. The method requires specification of only one variable in each bin size for condensation and evaporation calculations in an Eulerian drop-size framework. The results show that the variational method not only conserves the integral parameters of the spectrum, such as drop number, mean radius, liquid water content and the effective radius, but also provides very accurate calculation of the spectrum itself

Clustering Ensemble Meets Low-rank Tensor Approximation

This paper explores the problem of clustering ensemble, which aims to combine multiple base clusterings to produce better performance than that of the individual one. The existing clustering ensemble methods generally construct a co-association matrix, which indicates the pairwise similarity between samples, as the weighted linear combination of the connective matrices from different base clusterings, and the resulting co-association matrix is then adopted as the input of an off-the-shelf clustering algorithm, e.g., spectral clustering. However, the co-association matrix may be dominated by poor base clusterings, resulting in inferior performance. In this paper, we propose a novel low-rank tensor approximation-based method to solve the problem from a global perspective. Specifically, by inspecting whether two samples are clustered to an identical cluster under different base clusterings, we derive a coherent-link matrix, which contains limited but highly reliable relationships between samples. We then stack the coherent-link matrix and the co-association matrix to form a three-dimensional tensor, the low-rankness property of which is further explored to propagate the information of the coherent-link matrix to the co-association matrix, producing a refined co-association matrix. We formulate the proposed method as a convex constrained optimization problem and solve it efficiently. Experimental results over 7 benchmark data sets show that the proposed model achieves a breakthrough in clustering performance, compared with 12 state-of-the-art methods. To the best of our knowledge, this is the first work to explore the potential of low-rank tensor on clustering ensemble, which is fundamentally different from previous approaches

Adaptation of Dominant Species to Drought in the Inner Mongolia Grassland – Species Level and Functional Type Level Analysis

The adaptation of plants to drought through the adjustment of their leaf functional traits is a hot topic in plant ecology. However, while there is a good understanding of how individual species adapt to drought in this way, the way in which different functional types adapt to drought along a precipitation gradient remains poorly understood. In this study, we sampled 22 sites along a precipitation gradient in the Inner Mongolia grassland and measured eight leaf functional traits across 39 dominant species to determine the adaptive strategies of plant leaves to drought at the species and plant functional type levels. We found that leaf functional traits were mainly influenced by both aridity and phylogeny at the species level. There were four types of leaf adaptations to drought at the functional type level: adjusting the carbon-nitrogen ratio, the specific leaf area, the nitrogen content, and the specific leaf area and leaf nitrogen content simultaneously. These findings indicate that there is the trade-offs relationship between water and nitrogen acquisition as the level of drought increases, which is consistent with the worldwide leaf economics spectrum. In this study, we highlighted that the leaf economic spectrum can be adopted to reveal the adaptations of plants to drought in the Inner Mongolia grassland

New Polarization Basis for Polarimetric Phased Array Weather Radar: Theory and Polarimetric Variables Measurement

A novel scheme is developed for mitigating measurement biases in agile-beam polarimetric phased array weather radar. Based on the orthogonal Huygens source dual-polarized element model, a polarization measurement basis for planar polarimetric phased array radar (PPAR) is proposed. The proposed polarization basis is orthogonal to itself after a 90° rotation along the array’s broadside and can well measure the characteristics of dual-polarized element. With polarimetric measurements being undertaken in this polarization basis, the measurement biases caused by the unsymmetrical projections of dual-polarized element’s fields onto the local horizontal and vertical directions of radiated beam can be mitigated. Polarimetric variables for precipitation estimation and classification are derived from the scattering covariance matrix in horizontal and vertical polarization basis. In addition, the estimates of these parameters based on the time series data acquired with the new polarization basis are also investigated. Finally, autocorrelation methods for both the alternate transmission and simultaneous reception mode and the simultaneous transmission and simultaneous reception mode are developed

Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series

Theoretic analysis shows that the output power of the distributed generation system is nonlinear and chaotic. And it is coupled with the microenvironment meteorological data. Chaos is an inherent property of nonlinear dynamic system. A predicator of the output power of the distributed generation system is to establish a nonlinear model of the dynamic system based on real time series in the reconstructed phase space. Firstly, chaos should be detected and quantified for the intensive studies of nonlinear systems. If the largest Lyapunov exponent is positive, the dynamical system must be chaotic. Then, the embedding dimension and the delay time are chosen based on the improved C-C method. The attractor of chaotic power time series can be reconstructed based on the embedding dimension and delay time in the phase space. By now, the neural network can be trained based on the training samples, which are observed from the distributed generation system. The neural network model will approximate the curve of output power adequately. Experimental results show that the maximum power point of the distributed generation system will be predicted based on the meteorological data. The system can be controlled effectively based on the prediction