RBF Neural Network Combined with Knowledge Mining Based on Environment Simulation Applied for Photovoltaic Generation Forecasting

Photovoltaic generation forecasting is one of the main tasks of the planning and operation in power system. Especially with the development of mico-grid, relative study on renewable energy generation gain more and more concerns. In this paper, a short-term forecasting model combining knowledge and intelligent algorithm is developed for photovoltaic array generation. Self-organizing map (SOM) is proposed to extract the relative knowledge, and to choose the most similar history situation and efficient data for wind power forecasting with numerical weather prediction (NWP). The historical data is classified into several groups, though which we could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy radial basis function network (RBF) trained by the class of the forecasting day is adopted to forecast the photovoltaic output accordingly. A case study is conducted to verify the effectiveness and the accuracy. Compared with the conventional BP neural network, the forecasting results demonstrate the method proposed in this paper can gain better forecasting performance with higher accuracy

Spectroscopic methodologies and molecular docking studies on the interaction of the soluble guanylate cyclase stimulator riociguat with human serum albumin

Publisher's version (útgefin grein)Abstract Interaction of riociguat with human serum albumin (HSA) is extremely important in understanding the drug's disposition and efficiency. In the current study, the binding of riociguat to HSA was explored using spectroscopic methods and molecular docking. The quenching constant, the binding constant, the number of binding sites, thermodynamic parameters, and the secondary structure of protein were determined. A fluorescence study revealed that riociguat quenched HSA fluorescence via static quenching with a binding constant of 1.55 × 104 L mol-1 at 298 K. The calculated thermodynamic parameters indicated that the binding process was spontaneous and that the main interaction force was hydrophobic interaction. Site marker competitive binding experiments and molecular docking studies suggested that riociguat was inserted into the subdomain IIA (site I) of HSA. Alterations in the protein secondary structure after drug complexation were predicted. Results indicated that the protein a-helix structure increased with an increasing concentration of riociguat. This indicated that a riociguat-HSA complex was formed and that the protein secondary structure was altered by the addition of riociguat.This work was supported by the Natural Science Foundation of China (81502921 and 81503251), the Key Research and Development Program of Shandong Province (2017GSF218049), and Young Scholars Program of Shandong University (2015WLJH50).Peer Reviewe

Effect of Maillard Reaction on Tropomyosin Immunoreactivity in Mactra veneriformis

In this study, xylose and arabinose were subjected separately to Maillard reaction with a crude extract of Mactra veneriformis under dry-heating conditions. The immunoreactivity and digestion properties of the Maillard reaction products (MRPs) were analyzed, finding that the Maillard reaction could reduce the immunoreactivity of allergens derived from Mactra veneriformis, increase the continuous digestion rate of the crude extract in simulated gastrointestinal fluid, and reduce the particle diameter of the digestion products. After that, TM in the MRPs was separated and purified, and its structural characteristics and immunoreactivity were analyzed. The results showed that the α-helix content of TM decreased and the β-sheet, β-turn, and random coil contents increased after the Maillard reaction, the surface hydrophobicity increased, and the spatial structure changed, which eventually led to a reduction in the immunoreactivity of TM. This study provides a theoretical basis for the development of hypoallergenic clam products

Simulating the 1998 spring bloom in Lake Michigan using a coupled physical‐biological model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95047/1/jgrc12526.pd

Phonon-assisted radiofrequency absorption by gold nanoparticles resulting in hyperthermia

It is suggested that in gold nanoparticles (GNPs) of about 5 nm sizes used in the radiofrequency (RF) hyperthermia, an absorption of the RF photon by the Fermi electron occurs with involvement of the longitudinal acoustic vibrational mode (LAVM), the dominating one in the distribution of vibrational density of states (VDOS). This physical mechanism helps to explain two observed phenomena: the size dependence of the heating rate (HR) in GNPs and reduced heat production in aggregated GNPs. The argumentation proceeds within the one-electron approximation, taking into account the discretenesses of energies and momenta of both electrons and LAVMs. The heating of GNPs is thought to consist of two consecutive processes: first, the Fermi electron absorbs simultaneously the RF photon and the LAVM available in the GNP; hereafter the excited electron gets relaxed within the GNP's boundary, exciting a LAVM with the energy higher than that of the previously absorbed LAVM. GNPs containing the Ta and/or Fe impurities are proposed for the RF hyperthermia as promising heaters with enhanced HRs, and GNPs with rare-earth impurity atoms are also brought into consideration. It is shown why the maximum HR values should be expected in GNPs with about 5-7 nm size.Comment: proceedings at the NATO Advanced Research workshop FANEM-2015 (Minsk, May 25-27, 2015). To be published in the final form in: "Fundamental and Applied NanoElectroMagnetics" (Springer Science + Business Media B.V.

Knowledge Mining Based on Environmental Simulation Applied to Wind Farm Power Forecasting

Considering the inherent variability and uncertainty of wind power generation, in this study, a self-organizing map (SOM) combined with rough set theory clustering technique (RST) is proposed to extract the relative knowledge and to choose the most similar history situation and efficient data for wind power forecasting with numerical weather prediction (NWP). Through integrating the SOM and RST methods to cluster the historical data into several classes, the approach could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy echo state network (ESN) trained by the class of the forecasting day that is adopted to forecast the wind power output accordingly. The developed methods are applied to a case of power forecasting in a wind farm located in northwest of China with wind power data from April 1, 2008, to May 6, 2009. In order to verify its effectiveness, the performance of the proposed method is compared with the traditional backpropagation neural network (BP). The results demonstrated that knowledge mining led to a promising improvement in the performance for wind farm power forecasting

Knowledge Mining Based on Environmental Simulation Applied to Wind Farm Power Forecasting

Considering the inherent variability and uncertainty of wind power generation, in this study, a self-organizing map (SOM) combined with rough set theory clustering technique (RST) is proposed to extract the relative knowledge and to choose the most similar history situation and efficient data for wind power forecasting with numerical weather prediction (NWP). Through integrating the SOM and RST methods to cluster the historical data into several classes, the approach could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy echo state network (ESN) trained by the class of the forecasting day that is adopted to forecast the wind power output accordingly. The developed methods are applied to a case of power forecasting in a wind farm located in northwest of China with wind power data from April 1, 2008, to May 6, 2009. In order to verify its effectiveness, the performance of the proposed method is compared with the traditional backpropagation neural network (BP). The results demonstrated that knowledge mining led to a promising improvement in the performance for wind farm power forecasting

Intelligent Materials

Intelligence is the ability to learn from experience, comprehend complex situations, make choices, adapt, and act purposefully. Are the reported intelligent materials really intelligent?</p