The paper is concerned with a method intended for forecasting electric power consumption using discrete macromodels of daily and annual electric power consumption of defined objects. The method provides the possibility of estimating qualitative characteristics of future electric power consumption based on known prior data.
In this paper the possible directions of further development of science and technology are presented. The choice of these areas is subjective, and the demographics are taken into account, because people are simultaneously creators and consumers. Displaying advances in technology supported by science, without which these achievements would not have been. Attention is paid to the development of the economy, which in a general sense is identified with increasing prosperity.
The paper concerns the results of the quantitative study of dynamics for phosphates concentrations in the Small Carpathians rivers watersheds in Earthen Slovakia by using methods of nonlinear analysis and forecasting, chaos theory and dynamical systems. The conclusions can be viewed from the perspective of carrying out new algorithms for analysis and forecasting of the dynamics and evolution of anthropogenic water management landscape. Chaotic behaviour of the phosphates concentration time series in the watersheds of the Small Carpathians is studied.
In the article filed classification of forecasting methods. Displaying approximate model calculation prediction. Are forecasted development of information product – based on mathematical software “Statistica”.
Procedures of data mining based on prediction of time series for land cadastre data are described in this article. Principles required for the development of the method of forecasting using time serious are examined. Mathematical model is developed. The task of predicting land resources use in Striyskyi Park in Lviv is technically realized.
The moving average method with the 4 samples window width is used to raise the weekly forecast of the US dollar exchange rate accuracy. The non-iterative artificial neural network with the radial basis functions is used for. In the end we got the forecast error less than 1%
The IAS "Forecast" is developed for forecasting the electricity consumption in the original production conditions at PJSC "Lvivoblenergo." The statistical and neural network methods are used for the input data verification; is enhanced the space dimensions extending methods for the incoming data to use them with the ANN with non-iterative training
Simulation is carried out by numerical analysis of the dynamic system equations by the Runge-Kutta method. Consistently described the construction of a model that takes into account both interspecies competitionand ranks of other factors: light, water-logging, age structure, rainfall, external influences. The results of simulation obtained on the created software. The possibilities of using the created model local level to ensure the development of an information and decision support in forest management.
A computer based decision support system is proposed the basic tasks of which are adaptive model constructing and forecasting of financial and economic processes. The system is developed with the use of system analysis principles, i.e. the possibility for taking into consideration of some stochastic and information uncertainties, forming alternatives for models and forecasts, and tracking of the computing procedures correctness during all stages of data processing.
The article suggests and describes a GMDH algorithm for the synthesis of co-operative immune network in the solution of tasks of forecasting of time series. Conducted comparative experiments have shown that the use of external criteria improves adaptability, robustness and accuracy of the obtained solutions.