Method and cyber-physical system for forecasting and optimizing electricity consumption in residential districts

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dc.contributor.authorPysmeniuk, Volodymyr
dc.contributor.authorLevashenko, Vitaly
dc.date.accessioned2025-05-19T18:24:50Z
dc.date.available2025-05-19T18:24:50Z
dc.date.issued2025
dc.description.abstractThe development of cyber-physical systems combined with machine learning algorithms opens new opportunities for forecasting and optimizing electricity consumption in residential districts. This study examined existing technologies and solutions for energy consumption management, identifying their advantages and disadvantages. The analysis showed that modern commercial systems are primarily designed either for industrial use or individual consumption, lacking a comprehensive approach for residential districts. The proposed forecasting and optimization method is based on hybrid machine learning algorithms. For energy consumption forecasting, a combination of recurrent neural networks (RNN) and XGBoost was used, allowing for the consideration of both temporal dependencies and nonlinear factors. For energy consumption optimization, a combination of genetic algorithms (GA) and particle swarm optimization (PSO) was implemented, ensuring efficiency in finding optimal solutions. The developed cyber-physical system includes sensors for data collection, microcontrollers (Raspberry Pi) for data processing, and intelligent systems for controlling electrical appliances. This enables real-time energy consumption analysis and management, improving the energy efficiency of residential districts. Experimental results confirmed the effectiveness of the proposed approach, demonstrating high accuracy in energy consumption forecasting and the potential for reducing electricity costs through optimized usage. The proposed method has significant potential for scaling and implementation in large residential complexes, contributing to sustainable development and reducing the load on energy grids. Thus, the results of this study can be used for further improvement of energy management systems, promoting efficient electricity use, reducing consumer costs, and minimizing the environmental impact of energy systems.
dc.identifier.citationPysmeniuk V. Method and cyber-physical system for forecasting and optimizing electricity consumption in residential districts / V. Pysmeniuk, V. Levashenko // Computer Systems and Information Technologies. – 2025. – № 1. – P. 135-140.
dc.identifier.urihttps://elar.khmnu.edu.ua/handle/123456789/18346
dc.language.isoen
dc.publisherХмельницький національний університет
dc.subjectelectricity consumption forecasting
dc.subjectcyber-physical system
dc.subjectmachine learning
dc.subjectenergy consumption optimization
dc.subjectartificial intelligence
dc.subjectsmart grids
dc.subjectsensors
dc.subjectmicrocontrollers
dc.subjectenergy efficiency
dc.subjectoptimization algorithms
dc.subject.udc004.9
dc.titleMethod and cyber-physical system for forecasting and optimizing electricity consumption in residential districts
dc.typeСтаття
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