Abstract
Methods of improving the quality of regulation of technological parameters by combining various intelligent control algorithms in one automation system, which helps to reduce energy costs by 10-13%, are analyzed. It has been established that heating and ventilation systems have the highest energy consumption for indoor buildings (on average, more than 4,000 m3 of natural gas and almost 1,000 kWh of electricity are consumed per day for heating and ventilation in an industrial greenhouse. Correlation analysis of links between external disturbances and energy costs that ensure compliance with the technology of plant production, confirmed the hypothesis of conditions of uncertainty in the operation of industrial greenhouses are formed by random disturbances, incomplete information about the biological component, with linear correlation coefficients not exceeding r<0.35. both for forecasting energy costs and for the formation of energy efficient management strategies. Based on the use of fuzzy logic methods and genetic algorithm, models for finding and using optimal parameters of PI controller settings adapted to changes in the operating conditions of the automation system have been developed and studied. This provides better regulation in conditions of uncertainty, the time of regulation, over-regulation is reduced by two to three times. To create an energy-efficient microclimate management system in industrial greenhouses, operating in conditions of uncertainty, a neural network model for predicting the energy consumption of natural gas and electricity has been developed. The input parameters of the neural network forecasting model are: the value of external and internal air temperatures of the greenhouse, the value of relative humidity, the solar radiation absorbed by the greenhouse and the level of carbon dioxide in the greenhouse. The outputs of the forecasting model are the values of natural gas and electricity costs. The structural and functional scheme of the temperature and humidity control automation system in industrial greenhouses has been improved by combining intelligent algorithms for stabilizing the operation of technological equipment at the lower management level and optimizing energy costs by forecasting them at the upper level. The introduction of such a system saves up to 13% on natural gas for heating and up to 10% on electricity.