Year: 2025
Type: conference-paper
Abstract: Heating, ventilation and air conditioning systems are the biggest energy consumers in buildings. A more efficient system, which would consume less energy, can be achieved by timely fault detection. Increasing attention is being paid to different methods for fault detection and diagnosis in heating, ventilation and air conditioning systems due to the adoption of building automation systems and the advancement of data and machine learning techniques. These methods can be divided into model-based, knowledge-based, and data-driven methods. The paper provides an overview of different methods, and the greatest attention is paid to data-driven methods due to their high accuracy and faster and cheaper model development.
Authors: Univerzitet u Istočnom Sarajevu, Fakultet za proizvodnju i menadžment Trebinje Bosna i Hercegovina, Milica Kašiković, Aleksandar Anđelković, Univerzitet u Novom Sadu, Fakultet tehničkih nauka, Novi Sad
Institutions: University of East Sarajevo, University of Novi Sad
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4407704255