OPTIMIZATION OF THE STRUCTURE OF THE ENERGY CONSUMPTION FORECASTING SYSTEM WITH ATYPICAL ENERGY CONSUMPTION PATTERNS

Abstract

The creation of an intelligent energy consumption forecasting device for consumers with atypical energy consumption is considered, depending on the required forecast accuracy, taking into account, in addition to the target parameters of the power grid (P, Q), technological processes of enterprises, influencing factors: socio-economic (hour of the day; day of the week; ordinal number of the day in the year; sign of a holiday or mass events d); meteorological: (wind-cold index). The model refers to intelligent devices for adaptive forecasting of power consumption modes of the electric grid based on a multilayer neural network. The article is devoted to the choice of the optimal architecture of the neural network (NN) and the method of its training, providing forecasting with the least error. A multifactional model of power consumption based on a multilayer NN has been synthesized and tested. Within the framework of the conducted research, an NN model was built describing the architecture of a cyber-physical system (CFS) for forecasting power consumption. It has been established that for each consumer, due to significant differences in the nature of energy consumption, it is necessary to experimentally select network parameters in order to achieve a minimum prediction error. It is shown that with atypical power consumption, i.e., not repeated over time periods (hour, day, week, etc.), artificial intelligence and deep machine learning methods are an effective tool for solving poorly formalized or non-formalized tasks. The developed model has acceptable accuracy (MSE deviation up to 15%). To increase the accuracy of the forecast, it is necessary to carry out a regular refinement of the model and adjust it to the actual situation, taking into account new additive factors affecting the electricity consumption curve. The possibility of using this device in the technological management systems of regional grid companies, which forms the basis of a hierarchical automated information measuring system for monitoring and accounting for electricity, by accounting and forecasting the active and reactive power of electric consumers