Evaluation of ENERGY Based on current and projected energy balance ENTERPRISES

Evaluation of ENERGY Based on current and projected energy balance ENTERPRISES
Deyko B. V.
Date of presentation
Ph.D., Associate Professor
Inshekov E.M.

Master's thesis "Assessment of energy performance based on current and forecast energy balances enterprise" consists of 128 printed pages, 19 figures, 8 tables, 5 applications and contains 44 bibliographic titles for references. The purpose of the master's thesis is to improve the quality of analysis of energy efficiency by extending the theoretical foundations and practical approaches to the design and analysis of current and projected energy balance sheet. Used such research methods as descriptive theoretical, simulation using the software environment MATLAB. The originality of the work is in the fact that in the master's thesis was further developed methodology for performance assessment of energy efficiency in the synthesis of analytical tools to analyze the structure of energy industrial enterprise such as graphical analysis, comparative analysis and analysis of energy efficiency using the coefficient of useful factor energy sources. It was also proposed new forecasting model of daily power consumption of enterprise engineering system using artificial neural networks, which unlike existing forecasting is more accurate and does not need to build their accumulation of data on the impact of external factors on the level of power consumption. The thesis presents the results of forecasting consumption of electric energy engineering company. By the computer simulation and implemented forecasting algorithms in MATLAB. The approach, which proposed in thesis to the use of neural networks allows with the lowest cost of time getting enough accurate prediction of energy for the future, which in turn simplifies the construction of the planned and future energy balances. In the master’s thesis used programs such as Microsoft Word, Microsoft Excel, Matlab. Keywords: energy balance, fuel and energy resources, loss of energy, energy efficiency, energy efficiency indicators, the neural network.