HVAC System Performance Modeling Using Component-Based Machine Learning

verfasst von

Seyed Azad Nabavi, Ueli Saluz, Marco Wolf, Philipp Geyer

Abstract

Heating Ventilation and Air Conditioning (HVAC) systems are responsible for a significant portion of building energy consumption, accounting for up to 38% and 12% of global energy consumption. Predicting energy consumption for HVAC systems is highly important in the early design phases due to their significant impact on energy use and user comfort. However, it is a challenging task due to the complex and dynamic nature of these systems requiring the effort of building simulation. The current state-of-the-art methods for modeling HVAC systems use one data-driven model created by machine learning for the whole HVAC system. In this way, the behavior of the individual HVAC components is neglected and the developed model will have limited explainability and generalizability. The novelty of this study is breaking down HVAC systems into three component categories, which are zone components, secondary HVAC components, and primary HVAC components to not only predict the energy performance of HVAC systems but also the dependencies among them. Then, we apply a component-based machine learning approach to create data-driven models for the HVAC components’ performance. A random forest regression algorithm as a machine learning component serves to predict the performance of HVAC system components in buildings. Machine learning models developed for each component is performing predictions in a hierarchy of receiving/delivering information from other components. In this hierarchical component model, the zone components model informs the secondary HVAC components model of heat distribution components, and the secondary HVAC components model informs the primary model of the heat supply. The random forest approach was highly accurate in predicting component performance, with R² values of 0.99 and 0.97 for peak heating demand and annual heating demand in the zones, and 0.99 and 0.87 for the maximum design flow rate and UA value of Secondary-HVAC systems. The primary model also had high performance, with an R² value of 0.98. Compared to conventional data-driven models generated by machine learning, this component-based approach allows for better error tracking and offers explainability. Furthermore, the ML models have high generalizability due to the limited number of parameters and their reusability in further systems.

Organisationseinheit(en)

Abteilung Gebäudetechnik

Typ

Konferenzaufsatz in Fachzeitschrift

Journal

Building Simulation Conference Proceedings

Band

18

Seiten

2680-2687

Anzahl der Seiten

8

ISSN

2522-2708

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Bauwesen, Architektur, Modellierung und Simulation, Angewandte Informatik

Ziele für nachhaltige Entwicklung

SDG 7 – Erschwingliche und saubere Energie

Elektronische Version(en)

https://doi.org/10.26868/25222708.2023.1531 (Zugang: Offen)