Kratkoročna prognoza potrošnje električne energije zasnovana na metodama veštačke inteligencije

The topic of this dissertation is a short-term load forecasting using artificial intelligence methods. Three new models with least squares support vector machines for nonlinear regression are proposed. First proposed model is a model with forecasting in two stages. This model use additioal feature, maximum daily load which is not known for day ahead. Forecating of maximum daily load is obtained in the first stage. This forecasted value is used in second stage, where forecasting of hourly load is done. Model with feature selection, using mutual information for selection criteria, is a second proposed model. This model tries to find an optimal feature set for a given problem. Forecasting model based on an incremental update scheme is a third proposed model. This model is based on the incremental update of the initial training set by adding new instances into it as soon as they become available and throwing out the old ones. Then the model is trained with new training set. By this approach the evolving nature of the load pattern is followed and the model performance is preserved and improved. For models evaluation, the forecasting of hourly loads for one year is done. Electrical consumption data for the City of Niš, which have about 260000 habitans and average daily demand of 182 MW, is used for testing. Double sesonal ARIMA and Holt-Winters as representatives of clasical models and artificial neural networks, least squares support vector machines and relevance vector machines as representatives of artificial models, are used for models evaluation. For a measure of accuracy, mean absolute percentage error, symetrical mean absolute percentage error, square root mean error and absolute percentage error are used. Obtained results show that the best model is model with incremental update scheme, followed by double sesonal ARIMA and artificial neural networks models. The worst results are obtained by relevance vector machines and double sesonal Holt-Winters models. It has been shown that the best model could be successfully used with the short-term load forecasting problem

STOCK MARKET TREND PREDICTION USING SUPPORT VECTOR MACHINES

The aim of the paper was to outline a trend prediction model for the BELEX15 stock market index of the Belgrade stock exchange based on Support Vector Machines (SVMs). The feature selection was carried out through the analysis of technical and macroeconomics indicators. In addition, the SVM method was compared with a "similar" one, the least squares support vector machines - LS-SVMs to analyze their classification precisions and complexity. The test results indicate that the SVMs outperform benchmarking models and are suitable for short-term stock market trend predictions

Kratkoročna prognoza potrošnje električne energije zasnovana na metodama veštačke inteligencije

The topic of this dissertation is a short-term load forecasting using artificial intelligence methods. Three new models with least squares support vector machines for nonlinear regression are proposed. First proposed model is a model with forecasting in two stages. This model use additioal feature, maximum daily load which is not known for day ahead. Forecating of maximum daily load is obtained in the first stage. This forecasted value is used in second stage, where forecasting of hourly load is done. Model with feature selection, using mutual information for selection criteria, is a second proposed model. This model tries to find an optimal feature set for a given problem. Forecasting model based on an incremental update scheme is a third proposed model. This model is based on the incremental update of the initial training set by adding new instances into it as soon as they become available and throwing out the old ones. Then the model is trained with new training set. By this approach the evolving nature of the load pattern is followed and the model performance is preserved and improved. For models evaluation, the forecasting of hourly loads for one year is done. Electrical consumption data for the City of Niš, which have about 260000 habitans and average daily demand of 182 MW, is used for testing. Double sesonal ARIMA and Holt-Winters as representatives of clasical models and artificial neural networks, least squares support vector machines and relevance vector machines as representatives of artificial models, are used for models evaluation. For a measure of accuracy, mean absolute percentage error, symetrical mean absolute percentage error, square root mean error and absolute percentage error are used. Obtained results show that the best model is model with incremental update scheme, followed by double sesonal ARIMA and artificial neural networks models. The worst results are obtained by relevance vector machines and double sesonal Holt-Winters models. It has been shown that the best model could be successfully used with the short-term load forecasting problem

Synthesis, solvent interactions and computational study of monocarbohydrazones

Carbohydrazones are compounds that are increasingly studied due to their wide potential biological activity. Monocarbohydrazones (mCHs), as one of the carbohydrazone derivatives, so far have been poorly investigated. For a more detailed study, in this paper, eighteen compounds of monocarbohydrazones (eight known and ten newly synthesized derivatives) were synthesized and characterized using NMR and IR spectroscopy. As carbohydrazones show E/Z isomerization caused by the presence of the imino group, some of the synthesized mCHs are in the form of a mixture of these two isomers. The effects of specific and nonspecific solvent-solute interactions on the UV absorption maxima shifts were evaluated using linear free energy relationships principles, i.e., using Kamlet-Taft's and Catalan's models. For more information about interactions between dissolved substance and the surrounding medium, correlations have been made with Hansen's solubility parameters. The influence of the structure on the spectral behavior of the compounds tested was interpreted using Hammett's equation. Experimentally obtained physicochemical properties of mCHs were compared to and confirmed with computational methods that included TD-DFT calculations and MP2 geometry optimizations. Graphic abstrac

Fluid boundaries shaping using the method of kinetic balance

Fluid flow in curved channels with various cross-sections, as a common problem in theoretical and applied fluid mechanics, is a very complex and quite undiscovered phenomenon. Defining the optimum shape of the fluid flow boundaries, which would ensure minimum undesirable phenomena, like "dead water" zones, unsteady fluid flow, etc., is one of the crucial hydraulic engineering’s task. Method of kinetic balance is described and used for this purpose, what is illustrated with few examples.

A detailed experimental and computational study of monocarbohydrazones

The substituent and solvent effect on intramolecular charge transfer (ICT) of twelve monocarbohydrazones (mCHs) were studied using experimental and theoretical methodology. The effects of specific and non-specific solvent-solute interactions on the UV-Vis absorption maxima shifts were evaluated using linear free energy relationships (LFERs) principles, i.e. using the Kamlet-Taft and Catalan models. Linear free energy relationships in the form of single substituent parameter equation (SSP) was used to analyze substituent effect on UV-Vis, NMR and pK(a) change. According to crystallographic data and quantum chemical calculations, the trans (E) form was found to be more stable. A photochromism of compounds with 2-hydroxyphenyl and 2-pyridylimino groups substituted at imine carbon atom results in E/Z isomerization due to creation of intermolecular hydrogen bond in E and Z form, respectively. Multiple stage mass spectrometry (MS-MSn) analysis was applied to define main fragmentation pathways. Furthermore, the experimental findings were interpreted with the aid of ab initio MP2/6-311 G(d,p) and time-dependent density functional (TD-DFT) methods. TD-DFT calculations were performed to quantify the efficiency of intramolecular charge transfer (ICT) with the aid of the charge-transfer distance (DCT) and the amount of transferred charge (QCT) calculation. It was found that both substituents and solvents influence electron density shift i.e. extent of conjugation, and affect ICT character in the course of excitation