РЕАЛИЗАЦИЯ ИНТЕЛЛЕКТУАЛЬНОЙ СИСТЕМЫ НЕПРЯМОГО ПРОГНОЗИРОВАНИЯ ВЫРАБАТЫВАЕМОЙ ЭЛЕКТРОЭНЕРГИИ СОЛНЕЧНОЙ ЭЛЕКТРОСТАНЦИИ КАК ПРОГРАММЫ ДЛЯ ЭВМ

Abstract

The forecasting of electric power generated by a solar power plant enables effective and safe control over electric networks which integrate a cluster of solar power plants. Penalty rates for the purchase of solar power at the day-ahead market, which deviates by more than 5 % of the maximum capacity of solar power plants from the provided hourly model of the day-ahead market of solar power generation, update the accuracy of the day-ahead market model through effective intelligent systems for forecasting solar power generation. It has been found that there is no accessible software for successful forecasting of solar power generation; the advisability and relevance of designing such software with an intelligent system have been shown based on the fi ndings of the examined existing software. The study developed, tested and implemented an intelligent system of indirect forecasting of solar power generation in the form of computer software designed based on a modifi ed fuzzy neural network with an attention mechanism. A class diagram and a block-modular architecture for computer software that implements an intelligent system of indirect forecasting of solar power generation were developed in UML notes using the Microsoft Visio CASE tool. A block-modular architecture provides the fl exibility of computer software. The computer software implementing an intelligent system of indirect forecasting for solar power generation was tested for effectiveness, robust results, and the advisability of its application for building a day-ahead market model. The SCADA database of a solar power plant can be easily integrated with an intelligent system of indirect forecasting of solar power generation.