MODERN METHODS OF ENERGY MANAGEMENT IN THE SMART SUSTAINABLE WORK ENVIRONMENT - YAKUBOVYCH, MAKSYM; LIASHKEVYCH, VASYL; SHUVAR, ROMAN (Herald of Khmelnytskyi National University. Technical sciences, 2025)
Justification. Modern technologies of the Internet of Things (IoT) and Artificial Intelligence (AI) open new opportunities for automating energy management. A Decision Support System (DSS) based on these technologies can analyze energy consumption in real-time, predict loads, and automatically make adjustments to achieve optimal energy consumption. Implementing a DSS for energy management in a smart sustainable workspace is a relevant and necessary step to enhance economic efficiency, ecological responsibility, and the company’s competitiveness. Materials and methods. The concept of a "smart sustainable workspace" is formulated as an office environment that uses advanced technologies to automate, optimize, and manage various aspects of office activities. Requirements for the decision support system to ensure energy efficiency in a smart sustainable workspace are formulated. Results. The article analyzes modern energy management methods in a smart sustainable workspace, which are based on using advanced technologies to improve energy efficiency and reduce electricity costs. Special emphasis is placed on methods of analyzing collected data to identify trends and anomalies in energy consumption, allowing for prompt responses to inefficient use of resources. Approaches to optimizing energy consumption using autoregressive models are investigated. Methods of monitoring and analyzing electricity consumption in real-time and ways to identify trends and anomalies in the use of energy resources are considered. Autoregressive models have demonstrated high accuracy in predicting electricity consumption, allowing for prompt responses to inefficient use of resources and reducing electricity costs. Conclusions. The study’s results emphasize the prospects of using autoregressive models to improve the efficiency of energy management in smart sustainable workspaces, contributing to the sustainable development of information technologies.