METHOD AND CYBER-PHYSICAL SYSTEM FOR FORECASTING AND OPTIMIZING ELECTRICITY CONSUMPTION IN RESIDENTIAL DISTRICTS BASED ON MACHINE LEARNING ALGORITHMS

Abstract

Electricity is a key resource in the modern world, essential for industries, medicine, transportation, and daily life. With the increasing demand for electricity and the necessity of its efficient use, there is a growing need for advanced technologies for monitoring, forecasting, and optimizing electricity consumption. One promising solution in this field is the implementation of cyber-physical systems that integrate hardware and software for data collection, analysis, and energy resource management. The development of artificial intelligence and machine learning has led to an increasing number of solutions integrating these technologies into energy management. This study aims to develop a method and a cyber-physical system for forecasting and optimizing electricity consumption in residential districts using machine learning algorithms.