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The measurement and analysis of the seasonal fluctuations in the production capacity generated by Photovoltaic Power System (PVs) are of a great importance and very useful in practice. They are an important factor that should not be overlooked in the forecasting production capacity. So it is significant to use them optimally and correctly. In the present paper we give a methodology for analyzing the seasonal fluctuations in the region of Southwestern Bulgaria and try to forecast the power generation capacity. Some statistical and numerical methods are used and applied. Renewable energy sources are the future of energy on Earth. [ABSTRACT FROM AUTHOR]

The generation of timely expanded load profiles for individual municipalities in the Republic of Bulgaria for the consumption of gas and electricity is of great importance in the optimal management of energy sources in the country. Power-to-Gas (PtG) technology could be used to convert electricity into hydrogen or methane in times of negative residual load. Such load profiles of some regions in the Republic of Bulgaria are made in the present study. There are used the big data and data mining for statistical follow-up analysis for combined energy sources. [ABSTRACT FROM AUTHOR]

Renewable energy sources are of great importance for the whole world-environmental, social, political, etc. This is one of the main priorities of the European Union. Investing in terrains for the construction of renewable energy is a risky investment because the conditions under which it must be built and operate are not completely unknown, i.e., there is uncertainty about the environment. The investments made can be both profitable and unprofitable. This research presents a mathematical model and a mobile application for the optimal solution to this problem. A multi-criteria optimization problem under conditions of uncertainty is solved, which, through appropriate scalarization and efficiency coefficients, is reduced to a single-criteria problem-a knapsack problem with Boolean variables, considering the stochastic nature of nature. The solving algorithms are implemented in a mobile application for the Android system in Java programming environments. [ABSTRACT FROM AUTHOR]

We live in uncertain times, with economies collapsing every day, businesses going bankrupt and investments failing. Large corporations have difficulty allocating their budgets. Small and medium-sized businesses are in an unstable period, and there is no guarantee of success. To secure themselves, many companies are starting work on more than one business project. However, this leads to uncertainty as to which of the used to rely on at a critical moment and, accordingly, leads to failure and losses. Providing a platform or business application through which corporations can safely choose the most profitable project to develop on is their salvation - minimal losses and maximum profits. This is exactly the type of project this work presents. [ABSTRACT FROM AUTHOR]

In this article, a stochastic model for prediction of microgrid photovoltaic power generation, using statistical and stochastic methods is presented. The study is performed in the following steps: Processing of a large database of historical data (Data Mining); Construction of a stochastic forecasting model; Reporting a symmetric mean absolute percentage error in forecasting. [ABSTRACT FROM AUTHOR]

This article offers estimation of the error in forecasting the consumption of the natural gas of the freight schedule per subscriber according to the seasons. Statistical and stochastic methods are used for the analysis and forecasts. [ABSTRACT FROM AUTHOR]

In this article is proposed a method for forecasting vehicle traffic intensity in urban conditions and provides an estimate of the forecasting error. For input data in this study are used: the number of vehicles that have passed through a location of the road, based on data collected by observations; meteorological conditions; the day of the week; the season. For this purpose, database processing methods, statistical and stochastic methods are used. [ABSTRACT FROM AUTHOR]

Forecasting is a special scientific study, the subject of which is the perspective for the development of the phenomenon. It is of great importance for many scientific and applied fields. This article presents a stochastic model for microgrid load forecasting. Statistical and stochastic methods are used for forecasting. [ABSTRACT FROM AUTHOR]

The paper presents the creation of a general mathematical model of a two-step optimization problem which finds the most energy efficient route where the battery and the electric engine of an electric-bicycle are used to minimum. In the first step, a multi-criteria problem is solved which finds the most efficient mode of operation of the battery and the engine in every segment part of the complete route. To solve this task a scalarization method is used (also known as weighted sum method). In the second step, a common most efficient route is found by applying a network optimization model. To find the solution a method of dynamic optimization is used - Bellman's Principle. The major part of the paper is dedicated to the mathematical models, the approaches and the demonstration of the models’ workability. For this reason, the generation of data is based on the a priori known information as well as on information which is periodic and stochastic in the time and space. [ABSTRACT FROM AUTHOR]

The paper presents the creation of a general mathematical model of a two-step optimization problem which finds the most-safer route of an electric-bicycle cyclist. The first step is a multi-criteria problem which finds the average risk in the different segments which forms the complete route. To solve the problem, a scalarization method is used (also known as weighted sum method). In the second step, the safer route is found by applying a network optimization model. To find the solution a method for dynamic optimization is applied (Bellman's Principle). The generation of data is based on the a priori known information as well as on information which is periodic and stochastic in the time and space. [ABSTRACT FROM AUTHOR]

Stochastic programming is an approach for modeling optimization problems that involve uncertainty. Whereas deterministic optimization problems are formulated with known parameters, real world problems almost invariably include parameters which are unknown at the time a decision should be made. When the parameters are uncertain, but assumed to lie in some given set of possible values, one might seek a solution that is feasible for all possible parameter choices and optimizes a given objective function. Such an approach is applied to mathematical model of optimization problem for storage stocks in commercial warehouses. Here probability distributions (e.g., of demand) could be estimated from data that have been collected over time. The aim is to find some policy that is feasible for all (or almost all) the possible parameter realizations and optimizes the expectation of some function of the decisions and the random variables. [ABSTRACT FROM AUTHOR]

The aim of this paper is to find an optimal replacement of equipment in the warehouse of the company. Real data for the machines are used and are processed with appropriate software. The mathematical model is made for this problem as problem for the shortest path from graph theory. The solution is made by Bellman's principle. [ABSTRACT FROM AUTHOR]

Various problems for finding diet are considered. By changing of the objective function and one of the constrains different linear optimization problems are obtained. Real examples for individual diet with respect to medical tests are solved for two persons. [ABSTRACT FROM AUTHOR]

Achieving maximal overall efficiency is a priority in all companies. This problem is formulated as a knap-sack problem and afterwards as a linear assignment problem. An automated system is created for solving of this problem. [ABSTRACT FROM AUTHOR]