IoT for Green Building Management

TL;DR

This paper explores how IoT sensors combined with signal processing and machine learning can create cost-effective building management systems that optimize energy use by understanding occupancy and human activity impacts.

Contribution

It provides an overview of IoT-based BMS integrating signal processing and machine learning, addressing a gap in comprehensive energy management solutions.

Findings

IoT sensors can accurately detect building occupancy.

Human activities significantly influence energy consumption.

Proposed methods can reduce building energy use effectively.

Abstract

Buildings consume 60% of global electricity. However, current building management systems (BMSs) are highly expensive and difficult to justify for small to medium-sized buildings. As such, the Internet of Things (IoT), which can monitor and collect a large amount of data on different contexts of a building and feed the data to the processor of the BMS, provides a new opportunity to integrate intelligence into the BMS to monitor and manage the energy consumption of the building in a cost-effective manner. Although an extensive literature is available on IoT based BMS and applications of signal processing techniques for some aspects of building energy management separately, detailed study on their integration to address the overall BMS is quite limited. As such, the proposed paper will address this gap by providing an overview of an IoT based BMS leveraging signal processing and machine learning techniques. It is demonstrated how to extract high-level building occupancy information through simple and low-cost IoT sensors and studied the impact of human activities on energy usage of a building, which can be exploited to design energy conservation measures to reduce the building's energy consumption.