# Data-driven Modelling of Smart Building Ventilation Subsystem

## TL;DR

This paper presents a data-driven black-box modeling approach using data mining techniques to analyze and control building ventilation systems, demonstrating its effectiveness over a year of real-world data for decision support.

## Contribution

It introduces a novel application of data mining with Symbolic Aggregate Approximation and Support Vector Machines for modeling ventilation subsystems in smart buildings.

## Key findings

- Effective behavior modeling of ventilation units
- Potential for fault detection and energy optimization
- Feasibility of online deployment in BMS

## Abstract

Considering the advances in building monitoring and control through networks of interconnected devices, effective handling of the associated rich data streams is becoming an important challenge. In many situations the application of conventional system identification or approximate grey-box models, partly theoretic and partly data-driven, is either unfeasible or unsuitable. The paper discusses and illustrates an application of black-box modelling achieved using data mining techniques with the purpose of smart building ventilation subsystem control. We present the implementation and evaluation of a data mining methodology on collected data over one year of operation. The case study is carried out on four air handling units of a modern campus building for preliminary decision support for facility managers. The data processing and learning framework is based on two steps: raw data streams are compressed using the Symbolic Aggregate Approximation method, followed by the resulting segments being input into a Support Vector Machine algorithm. The results are useful for deriving the behaviour of each equipment in various modi of operation and can be built upon for fault detection or energy efficiency applications. Challenges related to online operation within a commercial Building Management System are also discussed as the approach shows promise for deployment.