Simulation modeling of the functional twin of the microclimate control system of an intelligent building.

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Abstract

The presented work is dedicated to the development of an intelligent microclimate control system for buildings (an HVAC system). The research focuses on addressing the problem of insufficient adaptability of traditional approaches (PID controllers, knowledge-based systems) in the context of dynamically changing internal environmental parameters of a building. The main emphasis is on creating a hybrid method that combines the advantages of functional programming and artificial intelligence. The study examines issues of energy efficiency, accuracy in maintaining comfortable conditions for visitors of intelligent buildings, and the robustness of the HVAC system to external disturbances. A crucial task is to minimize operational costs while ensuring the safety and reliability of equipment operation. The presented research covers all stages of software development—from designing its architecture to practical testing. The core of the research is based on the approach of a functional twin implemented in Haskell. LSTM networks are used for forecasting, genetic algorithms for optimization, and the RETE algorithm for rule processing. Verification is conducted through simulation modeling, generating 1440 data points. The scientific novelty of the presented work lies in the application of a categorical-theoretic approach to model the functional twin, where each device (both sensors and actuators) is represented as a composition of pure functions. Results demonstrate a 14.7% reduction in energy consumption, an increase in the operational time within a comfortable range to 94.7%, and a threefold reduction in the switching frequency of the HVAC system modes. Practical significance is confirmed by a 15% decrease in operational costs and improved cyber resilience through the use of immutable data structures. The conclusions indicate that the combination of functional programming with a hybrid approach in artificial intelligence provides a balance of key system parameters. The proposed architecture can serve as a benchmark for integrating IoT and cyber-physical systems within the framework of Industry 4.0.

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