Fast machine learning for building management systems

Mshragi, Mohammed ORCID: https://orcid.org/0009-0003-6511-4594 and Petri, Ioan ORCID: https://orcid.org/0000-0002-1625-8247 2025. Fast machine learning for building management systems. Artificial Intelligence Review 58 (7) , 211. 10.1007/s10462-025-11226-6

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Abstract

Building management systems (BMSs) are increasingly integrating advanced machine learning (ML) and artificial intelligence (AI) capabilities to enhance operational efficiency and responsiveness. The transformation of BMSs involves a wide range of environmental, behavioural, economical and technical factors as well as optimum performance considerations in order to reach energy efficiency and for long term sustainability. Existing BMSs can only provide local adaptability by creating and managing information for a built asset lacking the capability to learn and adapt based on performance objectives. This research provides a comprehensive review of ML techniques in BMSs, with particular emphasis and demonstration of fast machine learning (FastML) techniques in a real-case study application. The study reviews optimization methods for ML algorithms, focusing on Long Short-Term Memory (LSTM) networks for energy consumption forecasting and exploring solutions that leverage hardware accelerators for low-latency and high-throughput processing. The High-Level Synthesis for Machine Learning (HLS4ML) framework facilitates deployment of fast machine learning models with BMSs, achieving substantial gains in hardware efficiency and inference speed in resource-constrained environments. Findings reveal that HLS4ML-optimized models maintain accuracy while offering computational efficiency through techniques like pruning and quantization, supporting real-time BMS applications. This research significantly contributes to the development of intelligent BMSs by integrating ML algorithms with advanced hardware solutions, ultimately improving energy management, occupant comfort, and safety in modern buildings.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Schools > Engineering
Publisher:	Springer
ISSN:	0269-2821
Date of First Compliant Deposit:	17 April 2025
Date of Acceptance:	4 April 2025
Last Modified:	22 Apr 2025 08:59
URI:	https://orca.cardiff.ac.uk/id/eprint/177795

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