employee from 01.01.2012 to 01.01.2025
Krasnoyarsk, Krasnoyarsk, Russian Federation
This paper describes the step-by-step creation of a neural network model that simulates the variations in the consumed heat power of a container greenhouse, as well as its application for processing experimental data. The work converts the experimental data obtained from an automatic measurement and storage system based on Arduino into a format suitable for further neural network training; constructs graphs displaying the data changes for visual control of parameter variations before the training process; forms input and output training vectors in the form of in.xlsl and out.xlsx files. The paper presents and analyzes graphs and coefficients that characterize the training process and the accuracy of the obtained neural network; examines and analyses the architecture of the neural network, including a description of the purpose of individual blocks involved in information processing and decision-making. The author conducts a comparison of the heat power variations in the container greenhouse obtained from the neural network simulation model and the experimental-analytical model. The functionality of the neural network model is tested and confirmed with data outside the range of parameter variations obtained during the experiment.
container greenhouse, thermal characteristics, automatic measurement and storage system, neural network simulation model, heat power, amount of heat.
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