Technical and economic optimization of fuel facilities – energy complex using elements of fuzzy logic and artificial neural networks

Abstract

This article discusses the possibilities of optimizing the fuel and energy complex by improving the accuracy of the forecast of the state in both the operational and medium-term periods: from an hour to several years. The model used to implement the mentioned energy efficiency improvement is based on the use of fuzzy neural networks together with the function of minimizing power losses in the elements of electric power systems. The Mamdani modification is used, which takes into account the database of previous periods of the system state, represented as static load characteristics. The organization of this approach allows you to take into account weakly formalized factors, thereby improving the quality of forecasting and dispatching management. At the same time, it is possible to use models for a wide class of objects, which is achieved due to its scalability. The estimation of forecasting accuracy of the proposed approach exceeds similar indicators of currently used statistical methods and regression implementations. These factors are direct economic drivers of reducing production costs.