Technological approaches to directed structure formation of construction nanocomposites with increased corrosion resistance.

Physico-chemical processes of structure formation in nanocomposite building materials are associated with transformations of binding matrices and reinforcing components. The efficiency of building composites in the designed structures depends on the accurate choice of the source components: nanobinders, fillers (aggregates) and manufacturing technology. Increased corrosion resistance of building materials is provided by optimal selection of nanobinders and fillers, by increased density and treatment of the structure surface with protective coatings. The manufacturing feasibilities for nanocomposites based on various raw materials, nanobinders (gypsum, cement, bitumen, polymer, etc.), and inclusion of various dispersed phases (nanofillers, natural and technogenic aggregates) expand the variety of building composite materials. The synergistic dynamism of the occurrence of geometrical regularity of nanostructures during the structure formation of binders correctly demonstrates the fractal concept. Fractal nanostructures of binders with a rough surface are formed according to mechanism of diffusion-limited aggregation

Using neural network for building short-term forecast of electricity load of LLC «Omsk energy retail company»

Relevance of the research is caused by the requirements of current legislation to «day-ahead» forecast of energy consumption in the market for wholesale electricity and capacity market participants (WECM). Most of electricity in Russia is produced by combustion of solid minerals. According to the report of JSC «System Operator of Unified Energy System» for 2015 the share of electricity production by the types of UES power plants in Russia is: 59,8 % for thermal power plants, 15, 6 % for wind and solar power plants, 19 % for nuclear power plants and 5,6 % for captive power plants. At the same time, one of the main problems associated with electric energy generation and its consumption is the problem of power balance maintenance. On the one hand, power delivery interruptions may occur when increasing planned load, on the other hand decrease in electric energy consumption will reduce the efficiency of the power plants, and increase the cost of electricity for wholesale electricity and capacity market participants, and for the end user. High accuracy in forecasting electricity consumption allows keeping power balance and using geo assets effectively to generate electricity, taking into account the specific character of the consumer. To solve these problems the wholesale market was introduced in Russia in 2004. It currently operates. The relevance of the discussed issue is caused by the current legislation of forecasting electricity consumption in the day-ahead market to the wholesale electricity and capacity market participants. The wholesale electricity and capacity market was introduced in 2006, since that time, many companies received the status of the subject of WECM. According to the rules of interaction between the subject of wholesale electricity and capacity market participants and OJSC «ATS», the subjects of wholesale electricity and capacity market participants are required to carry out daily hourly «day-ahead» forecast. To ensure the quality of forecasting electricity consumption, the subjects of wholesale electricity and capacity market participants should prepare a regulatory framework, to develop methodology for building electricity consumption forecast and calculate the risks associated with the accuracy of the models used. On the one hand, the complexity of the problem solved is characterized by occurrence of aggregate data of supply points, as it is not always possible for the subject of wholesale electricity and capacity market participants to collect the data on individual consumption of power facilities in hourly mode. On the other hand, the introduction of commercial accounting system can solve this problem by embedding a large investment for installation of the automated commercial power system, but as a rule, the subject of wholesale electricity and capacity market participants goes to such a long-term cost-payback. The main aim of the study is to apply the forecasting methodology using neural network for building predictive models for LLC «Omsk Energy Retail Company». The methods used in the study: Holt-Winters model, the ARIMA, neural networks, temperature and wind index. The results. The authors have considered the methods of constructing the predictive models, the path of their evolution since the launch of wholesale electricity and capacity market participants, and developed the method of constructing the forecast of «Omsk Energy Retail Company» using neural network, taking into account the temperature and wind index and allocation of common types of days by electric energy consumption