Publications by Andrew James Sonta

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Blind use patterns significantly impact visual comfort and energy efficiency in office buildings. To quantify this impact on building performance, blind control occupant behaviour models (BC-OBMs) are typically integrated into building performance simulati ...

Data-driven building energy modeling is an emerging solution to facilitate the implementation of energy-flexible buildings. However, its black-box nature hinders interpretation, including with respect to human-building interaction. This drawback may bring ...

Personalized Environmental Control Systems (PECS) can improve both comfort and energy efficiency by shifting indoor climate control toward localized, occupant-tailored comfort, unlike conventional systems that condition entire, partly unoccupied spaces uni ...

Recent advances in personalized sensing and comfort feedback have spurred the development of data-driven comfort models tailored to individual needs. However, because current models treat sequential comfort feedback independently, they are subject to unsta ...

Given the large amount of time we spend indoors, designing and operating buildings that are safe, comfortable, and conducive to productivity and well-being is essential. To achieve this goal, in the past decades, research has been conducted to investigate ...

Recent research has investigated the importance of both walkable urban design and social cohesion. Social cohesion has been shown to have broad social and health benefits, and scholars have hypothesized that walkable urban design can influence cohesion, th ...

We present the design and implementation of RECA, a novel human-centric recommender system for co-optimizing energy consumption, comfort and air quality in commercial buildings. Existing works generally optimize these objectives separately, or by only cont ...

We introduce a novel human-centric deep reinforcement learning recommender system designed to co-optimize energy consumption, thermal comfort, and air quality in commercial buildings. Existing approaches typically optimize these objectives separately or fo ...

Building demand flexibility is essential for balancing the grid as renewable energy generation increases. However, the limited adoption of flexibility-enabling technologies underscores the need for scalable solutions to evaluate flexibility. Despite increa ...

Andrew James Sonta

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Development of a blind control occupant behaviour model (BC-OBM) based on contextual and time-related factors through supervised machine learning approaches

OccuVAE: Integrating unsupervised occupancy inference in data-driven energy modeling for human-centric operation

Personalized environmental control systems (PECS): A systematic review of performance evaluation methods for thermal comfort, air quality and energy

Mind the Hazard: Modeling and Interpreting Comfort with Personalized Sensing

Ten questions concerning statistical data analysis in human-centric buildings research: A focus on thermal comfort investigations

Rethinking walkability: Exploring the relationship between urban form and neighborhood social cohesion

RECA: A Multi-Task Deep Reinforcement Learning-Based Recommender System for Co-Optimizing Energy, Comfort and Air Quality in Commercial Buildings

A multi-task deep reinforcement learning-based recommender system for co-optimizing energy, comfort, and air quality in commercial buildings with humans-in-the-loop

A Data-driven Framework for Occupant-centric Demand Flexibility Potential Evaluation at Scale