Poster Abstract

Ferdoash, Afreen; Saini, Shubham; Khurana, Jitesh; Singh, Amarjeet

doi:10.1145/2821650.2830301

Cited by 5 publications

(1 citation statement)

References 1 publication

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“…A study by Ferdoash et al [33] developed a framework to detect excessive airflow and determine the optimal starting time for precooling building HVAC systems. The authors used temperature sensors in two buildings and integrated the data with meteorological conditions to create basic models for HVAC energy reduction using linear regression and SVM.…”

Section: Machine Learning In the Sustainability Of Smart Buildingmentioning

confidence: 99%

Machine Learning Practices During the Operational Phase of Buildings: A Critical Review

Jaufer,

Kader,

Spalevic

et al. 2024

aeletters

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Machine Learning (ML) is gaining attention in civil engineering especially within operational phase of building life cycle. This phase is crucial for managing every energy aspect while ensuring occupant comfort. Previous ML experiments have explored occupant behavior, occupancy estimation, load prediction, defect detection, and Heating, Ventilation, and Air Conditioning (HVAC) system diagnostics. However, challenges such as ML transferability and limited literature on ML components for the operational phase hinder broader industry adoption. This critical review aims to assess the potential of ML in building operations, focusing on energy consumption, big data control, reinforcement learning, and thermal comfort modeling. By identifying knowledge gaps, the study recommends further research to leverage ML for sustainable energy consumption and occupant comfort. It highlights ML’s promising role in striking a balance between energy efficiency and occupant wellbeing.

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Section: Machine Learning In the Sustainability Of Smart Buildingmentioning

confidence: 99%

Machine Learning Practices During the Operational Phase of Buildings: A Critical Review

Jaufer,

Kader,

Spalevic

et al. 2024

aeletters

View full text Add to dashboard Cite

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Detecting anomalies within smart buildings using do-it-yourself internet of things

Barhamgi

Heravi²,

Kariyawasam³

et al. 2022

J Ambient Intell Human Comput

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Detecting anomalies at the time of happening is vital in environments like buildings and homes to identify potential cyber-attacks. This paper discussed the various mechanisms to detect anomalies as soon as they occur. We shed light on crucial considerations when building machine learning models. We constructed and gathered data from multiple self-build (DIY) IoT devices with different in-situ sensors and found effective ways to find the point, contextual and combine anomalies. We also discussed several challenges and potential solutions when dealing with sensing devices that produce data at different sampling rates and how we need to pre-process them in machine learning models. This paper also looks at the pros and cons of extracting sub-datasets based on environmental conditions.

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Deriving sequences of operation for air handling units through building performance optimization

Gunay

Newsham

Ashouri

et al. 2020

Journal of Building Performance Simulation

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Operational parameters of air handling units (AHUs) play an important role in the energy and comfort performance of commercial buildings. Current guidelines to determine these parameters are based on heuristics and are not informed by formal optimization. This paper presents a building performance optimization method to derive sequence of operations for multi-zone AHUs. To this end, 27 variants of a generic EnergyPlus office building model are built representing three levels of occupancy, envelope, and HVAC capacity scenarios. The supply temperature, morning start time, and economizer settings of the model are optimized for the 27 scenarios by using a genetic algorithm. The results highlight that optimal supply temperature setpoints transition from ∼ 12°C to a range of ∼ 16°C to ∼ 20°C over an outdoor temperature range of ∼ 20°C to ∼ 0°C. Energy savings are estimated as ∼ 20% relative to a reference case with a constant supply temperature and preheating/precooling period.

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Poster Abstract

Cited by 5 publications

References 1 publication

Machine Learning Practices During the Operational Phase of Buildings: A Critical Review

Machine Learning Practices During the Operational Phase of Buildings: A Critical Review

Detecting anomalies within smart buildings using do-it-yourself internet of things

Deriving sequences of operation for air handling units through building performance optimization

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