HETEROGENEOUS ENSEMBLE NEURAL NETWORK FOR FORECASTING THE STATE OF MULTI-ZONE HEATING FACILITIES
HETEROGENICZNA SIEĆ NEURONOWA DO PROGNOZOWANIA STANU WIELOSTREFOWYCH OBIEKTÓW GRZEWCZYCH
Yukhymchuk M. Dubovoi V. Harbar Z. Yeraliyeva B.
27 June 2025Politechnika Lubelska
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Srodowiska
2025#15Issue 294 - 99 pp.
The research is aimed at increasing the accuracy of forecasting the state of multi-zone thermal facilities. Such facilities include multi-room premises, multi-zone greenhouses, tunnel kilns for brick production, and others. The high inertia of such facilities reduces the effectiveness of ad hoc control. Modern proactive control systems based on forecasting are mainly based on using neural network training. However, to forecast the state of a specific multi-zone thermal facility, training the network requires a very large dataset, which is difficult to create and use. A combined neuro-structural method for forecasting the state of multi-zone thermal facilities is proposed, in which the structure of the neural model reflects the structure of the mutual influence of the facility zones. The research of the method has shown the possibility of ensuring sufficiently high forecast accuracy with a smaller size of the training dataset.
dataset , forecasting , multi-zone facility , neuro-structural method , training
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Vinnytsia National Technical University, Department of Computer Control Systems, Vinnytsia, Ukraine
Department of Pedagogy and Educational Management, Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University, Ukraine
Information Systems Department, Faculty of Information Technology, M. Kh. Dulaty Taraz Regional University, Taraz, Kazakhstan
Vinnytsia National Technical University
Department of Pedagogy and Educational Management
Information Systems Department
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