[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117388-en":3,"doc-seo-117388-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117388,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Fast machine learning for building management systems","Building management systems (BMSs) increasingly incorporate machine learning and artificial intelligence to improve operational efficiency and responsiveness in real environments. The document reviews how modern BMSs account for environmental, behavioural, economical and technical variables to achieve energy efficiency and long-term sustainability, overcoming limits of purely local adaptability. It presents FastML approaches in a real-case study, focusing on LSTM-based energy consumption forecasting and deploying models with HLS4ML. Results show that pruning and quantization preserve accuracy while enabling low-latency, high-throughput inference for resource-constrained, real-time building applications, supporting energy management and occupant comfort.","Fast machine learning for building management systems  \nMohammed Mshragi1 · Ioan Petri1  \nAccepted: 4 April 2025 © The Author(s) 2025  \nAbstract  \nBuilding 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 BMSscan 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.  \nKeywords Fast machine learning · Building management systems · Energy forecasting · High level specification languages · Building automation  \nAbbreviations  \nANN Artificial neural network  \nAI Artificial intelligence  \n􀀍 Mohammed Mshragi[mshragim@cardiff.ac.uk](mshragim@cardiff.ac.uk)  \n1 School of Engineering, Cardiff University, Cardiff, UK  \n1 3  \nBMS Building management system  \nBEMS Building energy management system  \nBAS Building automation system  \nBIM Building information modeling  \nCNN Convolutional neural network  \nFL Federated learning  \nHFL Horizontal federated learning  \nVFL Vertical federated learning  \nFDD Fault detection and diagnosis  \nFPGA Field-programmable gate array  \nHLS4ML High-level synthesis for machine learning HVAC Heating, ventilation, and air conditioning  \nIoT Internet of Things  \nLLM Large language model  \nLSTM Long short-term memory  \nML Machine learning  \nMPC Model predictive control  \nPCA Principal component analysis  \nRL Reinforcement learning  \nRNN Recurrent neural network  \nSSL Semi-supervised learning  \nTL Transfer learning  \nNILM Non-intrusive load monitoring  \nLDA Linear discriminant analysis  \nt-SNE t-Stochastic neighborhood embedding  \nSOM Self-organizing maps  \nGAs Genetic algorithms  \nICA Independent component analysis  \nGMM Gaussian mixture model  \nIAQ Indoor air quality  \nMLP Multilayer perceptron  \nNAS Neural architecture search  \nLR Logistic regression  \nSVR Support vector regression  \nAR Auto-regressive  \nRF Random Forest  \nXGBoost Extreme gradient boosting AdaBoost Adaptive boosting  \nARMA Auto-regressive moving average  \nRT Real-time  \nDQN Deep Q-network  \nDDQN Double deep Q-Network  \nSARSA State-Action-Reward-State-Action  \nAR Auto-regressive  \nGA Genetic algorithm  \nTLD Transfer learning domain  \nRCM  \nTL-CNN  \nTL-LSTM LNCS  \nQ-Learning DT-MPC SBNMFSOM  \nResource consumption model  \nTransfer learning convolutional neural network Transfer learning long short-term memory Springer Lecture Notes in Computer Science Quality learning algorithm  \nDecision tree model predictive control  \nSemi-binary nonnegative matrix factorization Self-organizing maps  \n1","cbCaitCfERP9uBEX","https://ap.wps.com/l/cbCaitCfERP9uBEX","pdf",4294565,1,48,"English","en",105,"# Abstract\n## Machine learning integration in BMS\n## FastML methods and optimization\n## Hardware deployment with HLS4ML\n## Findings and real-time implications","[{\"question\":\"What problem does this work address in building management systems?\",\"answer\":\"It addresses the limitation of traditional BMS approaches that only provide local adaptability and cannot learn or adapt based on performance objectives, especially under changing environmental and operational conditions.\"},{\"question\":\"Which techniques are highlighted for energy consumption forecasting?\",\"answer\":\"The work focuses on Long Short-Term Memory (LSTM) networks for forecasting energy consumption in BMS scenarios.\"},{\"question\":\"How does HLS4ML support fast machine learning deployment in BMS?\",\"answer\":\"HLS4ML enables deployment of FastML models by leveraging hardware acceleration, improving hardware efficiency and inference speed in resource-constrained environments using methods such as pruning and quantization.\"}]","Fast machine learning for building management systems | 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problem does this work address in building management systems?","Question",{"text":75,"@type":76},"It addresses the limitation of traditional BMS approaches that only provide local adaptability and cannot learn or adapt based on performance objectives, especially under changing environmental and operational conditions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which techniques are highlighted for energy consumption forecasting?",{"text":80,"@type":76},"The work focuses on Long Short-Term Memory (LSTM) networks for forecasting energy consumption in BMS scenarios.",{"name":82,"@type":73,"acceptedAnswer":83},"How does HLS4ML support fast machine learning deployment in BMS?",{"text":84,"@type":76},"HLS4ML enables deployment of FastML models by leveraging hardware acceleration, improving hardware efficiency and inference speed in resource-constrained environments using methods such as pruning and 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