Abstract
Prediction of the electrical load schedule of an electrical system is an important aspect for determining electrical loads, which ensures the correct selection and cost-effective operation of reactive power compensation devices and voltage control devices, as well as relay protection and automation. This article discusses methods for predicting electrical load using an artificial neural network. The problems of choosing the optimal architecture and algorithm of neural network training are considered. The methods of the best forecast accuracy are determined. A genetic algorithm based on the group method of data handling was chosen as the main calculation.
Highlights
Prediction of power consumption and the corresponding mode parameters is an important task in planning and controlling the operating modes, ensuring the correct operation of the system
This process can be implemented in several ways: a short-term forecast, an operational forecast, a medium-term forecast, a long-term forecast
Artificial neural networks are devices based on parallel processing of information by all links
Summary
Prediction of power consumption and the corresponding mode parameters is an important task in planning and controlling the operating modes, ensuring the correct operation of the system. This process can be implemented in several ways: a short-term forecast (from a few seconds to an hour), an operational forecast (from an hour to a week), a medium-term forecast (from a week to a year), a long-term forecast (from a year to 20 years). The most popular artificial neural network architecture for predicting electrical load is the forward propagation architecture. This network uses continuously evaluated features and teacher training. Forecasting and planning indicators of electricity consumption for large consumers allows you to manage the cost of purchasing electricity through regulating the loading of equipment through managing production processes, translating the main volumes of electricity consumption in hours with the lowest cost, thereby reducing the cost of production and the amount of payments to power supply organizations. [2]
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