Methodological and Applied Aspects of Artificial Intelligence in Energy Consumption Prediction
Year: 2025
Type: conference-paper
Abstract: This research analyzes the application of modern methods for energy consumption forecasting in the residential sector. Traditional statistical models show limitations in modeling complex consumer behaviors, while technologies based on machine learning (ML) and artificial intelligence (AI) demonstrate significantly improved accuracy and adaptability. The study encompasses a wide spectrum of methodologies – from conventional statistical approaches to cutting-edge generative algorithms, evaluating their applicability for personalized household solutions. Comparative analysis highlights the advantages of AI-based technologies in terms of precision and adaptability, positioning them as optimal for integration into intelligent energy consumption management systems. The results provide a foundation for improving energy efficiency and resource optimization in the context of the growing application of smart technologies in the residential sector.
Authors: Ivan Ivanov, Kalin Petrov, Todor Rachovski, Desislava Petrova
Institutions: Plovdiv University, Technical University of Gabrovo
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411160316